Journal of Software Engineering Research and Development, 2023, 11:8, doi: 10.5753/jserd.2023.2671 

  This work is licensed under a Creative Commons Attribution 4.0 International License.  

 

Identification and Management of Technical Debt:  

A Systematic Mapping Study Update 

María Isabel Murillo  [ University of Costa Rica | maria.murilloquintana@ucr.ac.cr] 

Gustavo López [ University of Costa Rica | gustavo.lopezherrera@ucr.ac.cr] 

Rodrigo Spínola [ Salvador University | rodrigo.spinola@unifacs.br] 

Julio Guzmán [ University of Costa Rica | julio.guzman@ucr.ac.cr] 

Nicolli Rios [ Federal University of Rio de Janeiro | nicolli@cos.ufrj.br] 

Alexia Pacheco [ University of Costa Rica | alexia.pacheco@ucr.ac.cr] 

 

Abstract 

Technical debt is a concept used to describe the lack of good practices during software development, leading 

to several problems and costs. Identification and management strategies can help reduce these difficulties. In a 
previous study, Alves et al. (2016) analyzed the research landscape of such strategies from 2010 to 2014. This 

paper replicates and updates their study to explore the evolution of technical debt identification and management 

research landscape over a decade, including literature from 2010 until 2022. We analyzed 117 papers from the 

ACM Digital Library, IEEE Xplore, Science Direct, and Springer Link. Newly suggested strategies include auto-
matically identifying admitted debt in comments, commits, and source code. Between 2015 and 2022, more em-

pirical evaluations have been performed, and the general research focus has changed to a more holistic approach. 

Therefore, the research area evolved and reached a new level of maturity compared to previous results from Alves 

et al. (2016). Not only are code aspects considered for technical debt, but other aspects have also been investigated 

(e.g., models for the development process).  

Keywords: Technical Debt Management, Technical Debt Identification, Software Development Process. 

 

1  Introduction 

Technical debt (TD) is the consequence of taking shortcuts 

during the software development process, providing short-

term benefits but potentially bringing more difficulties and 

costs in later stages (Izurieta et al., 2012). When developers 

take these shortcuts, deficiencies may be inserted. The cost 

of fixing previous work increases as the development contin-

ues because correcting the defects becomes more complex 

when technical debt is not timely paid (Akbarinasaji & 

Bener, 2016) 

The interest is the additional cost that may have to be as-

sumed because of the delayed payment. On the other hand, 

the principal is the amount over which interests are paid. In 

technical debt, the principal is the original cost of fixing the 

software (Ampatzoglou et al., 2015). When developers can-

not pay the existing technical debt, bankruptcy may occur 

(Akbarinasaji & Bener, 2016) 

Several activities can help manage debt during the soft-

ware development process. Management activities may in-

clude measuring, prioritizing, preventing, monitoring, doc-

umenting, communicating, and paying the debt. (Li et al., 

2015). The purpose of performing these actions is to avoid 

major problems that may lead to significant consequences, 

such as the failure of software projects.  

Management strategies can help determine the appropri-

ate time to pay the debt before interests become very costly. 

Consequently, it is possible to make faster deliveries in a 

controlled manner (Freire et al., 2020). Also, strategies al-

low even to recognize if the debt needs to be paid because 

there may be cases when there is no need to pay it, for 

example, when there is the certainty that a module will not 

change in the future (Guo et al., 2014).  

Technical debt management is complex because there 

may be uncertainty during software development. Also, 

many factors must be considered for its management, such 

as the present and future costs, as well as the risks that are 

implied (Guo et al., 2014).  

The identification of TD comprises the activities or ac-

tions taken to detect the presence of debt in software arti-

facts. Technical debt identification is the first step that needs 

to be taken to start managing it and avoid its possible high 

costs (Guo et al., 2014). For instance, TD identification is 

essential to prevent unwanted consequences of debt. 

Alves et al. (2016) investigated the technical debt identi-

fication and management landscape between 2010 and 2014 

by analyzing 100 primary studies. They found that strategies 

mainly addressed types of technical debt associated with 

source code. Nevertheless, few empirical evaluations 

demonstrated the proposals' actual benefits, limitations, and 

applicability. Alves et al. (2016) also presented an initial 

taxonomy of technical debt types and a list of indicators for 

their identification. In that study, TD management was un-

derstood as the activities that follow its identification. The 

findings of Alves et al. (2016) provided valuable contribu-

tions for both researchers and practitioners, while they also 

characterized the state of the art in the research area. 

In this paper, we update the mapping study of Alves et al. 

(2016) to find proposals done between 2015 and 2022 about 

managing and identifying technical debt. Additionally, this 

paper provides a comparison with the previous results ob-

tained by Alves et al. (2016) and an analysis of the research 

landscape comprising more than a decade. Keeping the re-

sults updated is essential because it helps to understand the 

https://orcid.org/0000-0002-8729-3867


 

Identification and Management of Technical Debt: A Systematic Study Update Murillo et al. 2023 

evolution of the research topic and new findings (Nepo-

muceno & Soares, 2019).  

We consider the application of the same research ques-

tions, search string, search strategy, sources, inclusion, and 

exclusion criteria as an update of the previous systematic 

mapping. We intend to answer the same research questions 

(without adaptations) since changing them could be consid-

ered a new mapping instead of an update (Nepomuceno & 

Soares, 2019). Likewise, we considered TD management as 

the activities following debt identification to be conceptu-

ally consistent. The main difference in the protocol was the 

time-frame delimitation since our update only included pa-

pers published after the original study’s year of inclusion. 

We also provide a more detailed definition than the original 

study of what we considered as “general” technical debt pa-

pers for their classification. Furthermore, two original au-

thors assisted in the update process to ensure the compati-

bility between the concepts, procedure, and results of both 

systematic studies. 

The high-level research question we aim to answer is: 

• What strategies have been proposed to identify or man-

age technical debt in software projects?  

Similarly, the complementary research questions are: 

• RQ1. What are the types of technical debt found in the 

literature? 

• RQ2. What are the strategies proposed to identify tech-

nical debt? 

o RQ2.1. Which empirical evaluations have been per-

formed? 

o RQ2.2. Which artifacts and data sources have been 

proposed to identify technical debt? 

o RQ2.3. Which software visualization techniques 

have been proposed to identify technical debt? 

• RQ3. What strategies have been proposed for the man-

agement of technical debt? 

o RQ3.1. Which empirical evaluations have been per-

formed? 

o RQ3.2. Which software visualization techniques 

have been proposed to manage technical debt? 

We analyzed 117 primary studies and identified new pro-

posals and indicators between 2015 and 2022. Empirical 

evaluations of the analyzed papers include case studies, 

controlled experiments, and action research, but more eval-

uations are still required. Also, we found that technical debt 

visualization is yet an area that researchers have not exten-

sively studied. This is a relevant finding since visualization 

techniques may be useful to aid decision-making for TD. 

This paper’s results benefit researchers since we provide 

knowledge about state-of-the-art and open problems that are 

future research opportunities. It is also helpful to practition-

ers since we present identification, management, and visual-

ization strategies applicable to software projects to prevent 

technical debt unwanted negative consequences. 

Future research opportunities include investigating new 

ways to use developers’ knowledge about debt (not only 

through commits and comments) and exploring new 

strategies with a less-technical approach (such as incentives 

and TD guilds). Moreover, analyzing the applicability of 

strategies in different contexts, such as public or private or-

ganizations, is a future research opportunity. 

The structure of this article is as follows. Section 2 de-

scribes previous literature related to this work. Section 3 pre-

sents the methodology used to perform the literature review. 

Section 4 presents the results obtained. Section 5 includes 

the discussion, Section 6 the threats to validity, and Section 

7 covers the conclusions. 

2 Related work 

This section presents several previous works performed by 

other authors, particularly those that have addressed the 

identification and management of technical debt during soft-

ware development. Table 1 gives an overview of each au-

thors’ contributions. 

Rios et al. performed a tertiary study to identify the state 

of the art regarding technical debt between 2012 and 2018 

(Rios et al., 2018). The authors studied the understanding of 

the technical debt concept and the research efforts on its 

identification and management. They found nine secondary 

studies about technical debt management and two regarding 

its identification. Until 2018 there was little knowledge 

about the benefits and limitations of the proposed manage-

ment strategies and indicators.  

Another systematic mapping studied the concept of tech-

nical debt and its management activities and tools (Li et al., 

2015). The authors analyzed 94 studies published between 

1992 and 2013 and identified activities and tools for tech-

nical debt. Some of the mentioned tools are Checkstyle, 

DebtFlag, SonarQube, CodeVizard, and FindBugs. Like-

wise, the activities include code analysis, cost categoriza-

tion, calculation models, code metrics, and portfolio ap-

proach. Also, the authors proposed a classification of tech-

nical debt types and argued that there needs to be more liter-

ature about what should not be considered technical debt.  

The work of Fernández-Sánchez et al. consisted of a sys-

tematic mapping to identify the elements to consider for the 

management of technical debt, based on the literature until 

2015 (Fernández-Sánchez et al., 2017). The authors identi-

fied the main aspects of technical debt management. They 

found that the business organizational perspective has not 

been addressed much in the literature, while research has fo-

cused more on the technical point of view.   

Another systematic literature review focused on technical 

debt in the digital government area (Nielsen et al., 2020). This 

paper aimed to discover what fields of technical debt manage-

ment are being studied and the focus of the performed re-

search. The authors analyzed 31 pieces, from which a third 

proposed a tool, method, technique, or model for technical 

debt management. The authors found several gaps, including 

a lack of research on the public sector and a limited abstraction 

level of the analyses. Authors conclude that technical debt 

management is mainly studied either on open software pro-

jects or in the private sector.  



 

Identification and Management of Technical Debt: A Systematic Study Update Murillo et al. 2023 

Macit et al. performed a systematic mapping study regard-

ing methods for identifying architectural debt based on the 

analysis of 28 papers published between 2011 and 2020 

(Macit et al., 2020). The authors mention that architectural 

debt identification has been increasingly investigated in recent 

years. Also, code mining and expert opinion are common 

methods.  

Alfayez et al. performed a systematic literature review on 

technical debt prioritization (Alfayez et al., 2020). The authors 

aimed to identify the current prioritization approaches, the de-

cision factors, and the artifacts on which these approaches are 

based. A total of 23 papers published between 1992 and 2018 

were analyzed. As a result, 24 strategies were found for tech-

nical debt prioritization. These approaches mainly addressed 

code, general, and design technical debt.  

Lenarduzzi et al. performed a literature review regarding 

strategies and tools for technical debt prioritization (Lenar-

duzzi et al., 2021). In this study, they analyzed 44 primary 

studies published until 2020. Code, architecture, and design 

were the most frequent types of technical debt addressed. The 

authors found a lack of consensus on the factors to consider 

when prioritizing and measuring technical debt. Also, they 

show a lack of validated and reliable tools for technical debt 

prioritization. 

Alves et al. present a systematic mapping regarding tech-

nical debt identification and management (Alves et al., 2016). 

In that study, the authors analyzed 100 primary studies, dis-

cussed a taxonomy of TD types, presented a list of the strate-

gies found in the literature, and created a list of indicators that 

can help identify technical debt.  

Table 1. Contributions by other authors. 

Authors Research topic Analyzed period Contributions 

Li et al., 2015 
Technical debt 

management 
1992 – 2013 

• Analyzed the technical debt concept on 94 existing research efforts. 

• Proposed a classification of ten technical debt types. 

• Identified the quality attributes compromised by technical debt. 

• Determined activities and tools for technical debt management. 

Alves et al., 2016 

Technical debt 

identification and 

management 

2010 - 2014 

• Analyzed 100 papers and determined a classification for technical debt 
types. 

• Listed strategies to identify or manage technical debt. 

• Determined the empirical evaluations, artifacts, and data sources cited 
in the literature for technical debt identification and management. 

Fernández-Sán-

chez et al., 2017 

Elements to man-

age technical debt 
2010 - 2015 

• Provided a taxonomy of elements for technical debt management by 
analyzing 63 papers. 

• Identified the proposed methods and techniques to manage technical 
debt. 

• Analyzed technical debt management elements from the perspective of 
stakeholders. 

Rios et al., 2018 Technical debt 2012 – 2018 

• Studied 13 secondary studies and their TD research topics. 

• Proposed a taxonomy of technical debt types. 

• Identified activities, strategies, and tools to support technical debt man-
agement. 

Nielsen et al., 

2020 

Technical debt 
management in 

digital govern-

ment 

2017 - 2020 

• Analyzed 31 papers about technical debt management research in the 
public sector. 

• Determined a research agenda for the digital government area. 

Alfayez et al., 

2020 

Technical debt 

prioritization 
1992 - 2018 

• Identified approaches and decision factors for technical debt prioritiza-
tion by studying 23 papers. 

• Analyzed the type of human involvement and artifacts needed for tech-
nical debt prioritization. 

Lenarduzzi et 

al., 2021 

Technical debt 

prioritization 
2011 - 2020 

• Determined the prioritization strategies for technical debt by analyzing 
44 primary studies. 

• Analyzed factors and measures considered for technical debt prioriti-
zation. 

• Identified tools for technical debt prioritization. 



 

Identification and Management of Technical Debt: A Systematic Study Update Murillo et al. 2023 

Three previous studies analyzed and proposed a classifi-

cation of technical debt types (Alves et al., 2016; Li et al., 

2015; Rios et al., 2018). However, there is still no consensus 

on these taxonomies. This paper does not aim to provide a 

consensus but to find if new TD types have been mentioned 

recently and should be considered for new or refined taxono-

mies. 

More recent efforts in the research area were those made 

by Nielsen et al. (2020) and Lenarduzzi et al. (2021). Their 

studies focused on technical debt prioritization and technical 

debt management in the digital government area. This paper 

focuses on technical debt identification and management, a re-

lated but different scope than their contributions. 

Alves et al. (2016) and Li et al. (2015) made previous ef-

forts specifically about technical debt identification or man-

agement. However, they analyzed literature published be-

tween 1992 and 2014. Our study aims to replicate and update 

the work of Alves et al. (2016) to integrate the obtained results 

by including papers published between 2015 and 2022. This 

delimitation is the main difference between this work and pre-

vious contributions made by other authors.  

The relevance of performing this study is justified by the 

application of the framework proposed by (Mendes et al., 

2020) for updating Systematic Literature Reviews: 

• Does the previous study still address a current question? 

The high-level research question of this paper is: 

What strategies have been proposed to identify or man-

age technical debt in software projects? Any software 

may contain technical debt issues, regardless of the de-

veloping company’s size or resources. The Consortium 

for Information and Software Quality (CISQ) reports 

that the cost of poor software quality in the US is at least 

$2.41 trillion and the accumulated technical debt princi-

pal is about $1.52 trillion in 2022 (Consortium for Infor-

mation & Software Quality, 2022). Therefore, TD re-

mains an expensive issue. Identifying and managing TD 

is still a major problem in the software development in-

dustry. Thus, investigating these topics is relevant for 

both practitioners and researchers. 

• Has the previous study had good access or use? 

The work of Alves et al. (2016) was published in the 

Information and Software Technology Journal and is 

fully available through the Science Direct library. By 

March 2023, this paper has 589 reads and 238 citations 

(according to ResearchGate metrics). Thus, the previous 

study has good access and use. 

• Has the previous study used valid methods and it was 

well conducted? 

Alves et al. (2016) based their methods on the stand-

ard process for conducting systematic mapping studies 

by Petersen et al. (2008). They provide a full explanation 

of the study implementation (research questions, search 

strategy, selection criteria, and classification scheme). 

The study presents a clear view of each step’s outcome. 

Hence, it provides sufficient details and data to replicate 

the procedures. Moreover, two of the original authors 

participated in the update process.  

• Are there any new relevant studies, methods, or new in-

formation? 

Research on technical debt is constantly being pub-

lished in different venues, such as conferences and jour-

nals. For example, the International Conference on 

Technical Debt (TechDebt) is held annually since 2018, 

which is two years after the publication of the previous 

study in 2016. Consequently, there are plenty of new 

pieces on TD. 

• Will the inclusion of new studies/information/data 

change the findings, conclusions, or credibility? 

Since the publication of the previous study in 2016, 

the concepts and focus of the TD research area have 

evolved. In this paper, we will discuss in detail these 

changes. One of the most important aspects is the in-

crease of research efforts that address technical debt 

management with a more holistic perspective. 

By updating the previous study by Alves et al. (2016), we pro-

vide the following contributions: 

• An analysis of the technical debt identification and 

management research landscape between 2010 and 

2022; 

• Analysis of the previously proposed technical debt 

types and identification of new potential types men-

tioned recently in the literature; 

• List of the strategies or techniques for technical debt 

identification, management, and visualization; 

• An analysis of the empirical evaluations of the pro-

posed methods, including the artifacts, programming 

language, and data sources used. 

• Discussion on technical debt concepts and their evolu-

tion from 2010 to 2022. 

The contributions presented herein provide insights to both 

practitioners and researchers regarding the most recent pro-

posals for identifying and managing technical debt. This may 

help for further industry application of new proposals and for 

finding new research opportunities. The following section 

presents the methodology applied to perform this study.  

3 Research Method 

This paper aims to analyze the research landscape on tech-

nical debt identification and management. This section de-

tails the search strategy, study selection process, and synthe-

sis methods. 

3.1 Research questions  

In this section, we present the rationale and importance of 

the research questions. 

• RQ1. What are the types of technical debt found in the 

literature? 

This question aims to determine if there are new tech-

nical debt types described in the literature different from 



 

Identification and Management of Technical Debt: A Systematic Study Update Murillo et al. 2023 

those proposed by Alves et al. (2016). Also, we aim to 

know which types of technical debt have been most studied 

in the literature between 2015 and 2022.  

This research question is important because there is still 

no consensus on the different technical debt types. We aim 

to analyze the evolution of these concepts between 2010 

and 2022 by integrating our results and those provided by 

Alves et al. (2016). However, the intent of this paper is not 

to establish a consensus but to find out the TD types men-

tioned in the literature. 

• RQ2. What are the strategies proposed to identify tech-

nical debt? 

This research question aims to determine new artifacts 

or data sources mentioned in the literature. Also, we aim to 

know which artifacts and data sources are the most cited. 

This allows us to determine trends or changes in recent 

years. We also aim to analyze the empirical evaluations of 

previously mentioned strategies since Alves et al. (2016) 

describe the need for more assessments to determine the 

applicability of such strategies.  

Visualization techniques for technical debt identification 

are also crucial because they may help communication be-

tween developers and stakeholders, affecting decision-

making during software development. 

• RQ3. What strategies have been proposed for the man-

agement of technical debt? 

This research question aims to determine the strategies 

for technical debt management and how they have been 

empirically tested to determine their applicability. Also, 

we aim to identify the visualization techniques proposed 

for technical debt management. Alves et al. (2016) found 

few visualization strategies. This study analyzes how this 

specific research topic has evolved from 2010 to 2022. 

3.2 Search Strategy    

We retrieved papers from the databases ACM Digital Li-

brary, IEEE Xplore, Science Direct, and Springer Link. We 

also consulted Engineering Village, Scopus, Citeseer, and 

DBLP, but no papers were included from these libraries. 

Since this paper updates the previous work of Alves et al. 

(2016), we used the same search string: 

(“Technical debt”) AND (“Software”) 

This search string was used in all the sources, restricting the 

results to publications between 2015 and 2022.  

3.2.1 Inclusion Criteria  

We considered papers that met the following inclusion crite-

ria: 

• Address the identification or management of technical 

debt in the context of software development; 

• Explain one or more strategies, techniques, or activities 

for identifying or managing technical debt; 

• The year of its publication is between 2015 and 2022 

since the previous work of Alves et al. (2016) included 

papers from earlier years. 
 

We also considered papers that address technical debt in a 

general manner or focus on a specific type of debt. 

Moreover, we included those that either provided empirical 

proof of their proposal or only a theoretical description.  

3.2.2 Exclusion Criteria  

Only the most recent paper was considered when several 

pieces reported the same study, and each study was consid-

ered separately when multiple studies were contained in a 

single paper. Also, we applied the next exclusion criteria: 

• Papers that do not specify how to identify or manage 

technical debt with a strategy, activity, or technique. 

Therefore, we excluded exploratory studies of technical 

debt management; 

• Papers in progress (incomplete) or those that do not pro-

vide full-text access;  

• Papers published before the year 2015; 

• Duplicate papers; 

• Papers published in a language different than English.  

Moreover, papers in the form of PowerPoint presentations, 

reports, and abstracts only were not considered. 

3.3 Study Selection    

The study selection was performed by following the fol-

lowing steps: 

• Search: The first step of the process was to perform the 

search using the defined search string on the databases 

(ACM Digital Library, IEEE Xplore, Science Direct, 

Engineering Village, Springer Link, Scopus, Citeseer, 

and DBLP). As a result, we found 2517 papers in total.  

• Identification (Filter 1): The second step was removing 

duplicate papers and applying the exclusion and inclu-

sion criteria. In total, 466 duplicate studies were identi-

fied, leaving a total of 2051 articles (without duplicates). 

• Screening (Filter 2): The next step was screening the ar-

ticles. We read each of the 2051 titles and abstracts to 

find those that comply with the eligibility criteria. In this 

step, we identified 209 studies. In this step, 1852 papers 

were excluded because they did not comply fully with 

the inclusion criteria. This is explained because the 

search string is generic and returned articles that are not 

relevant to this study. 

• Inclusion and analysis (Filter 3): All 209 papers were 

read in full at this stage. After reading them, only 111 

were selected using the eligibility criteria. At this stage, 

we also extracted data from the selected papers as de-

scribed in the following subsection (3.4 Synthesis Meth-

ods). 

• Backward snowballing: During the final stage, we re-

viewed the references of each of the studies. As a result, 

we included seven more papers, which were analyzed 

and combined with the results of the study selection. 

One researcher performed the search, identification, screen-

ing, and inclusion for every paper. Later, two other research-

ers were randomly assigned a set of papers each to inde-

pendently review each paper and extract the data. The results 

were compared and discussed in case of disagreement. The 

process was performed in mid-March 2022. 



 

Identification and Management of Technical Debt: A Systematic Study Update Murillo et al. 2023 

3.4 Synthesis Methods 

From each of the included papers, we extracted six cate-

gories of information. Table 2 summarizes the data variables 

collected. 

• Metadata: For a demographic characterization, we col-

lected the title, authors, type and year of publication, and 

digital library from each included paper. These data were 

extracted as explicitly found on each corresponding da-

tabase. We also considered two research topics: identifi-

cation or management of technical debt. From each pa-

per, we also collected the corresponding research topic. 

• Technical debt types: We documented each paper’s ad-

dressed type of technical debt. Some papers explicitly 

mentioned the studied type of technical debt, but in oth-

ers, this was implicit. Also, many papers addressed tech-

nical debt without focusing on any specific type (77 in 

total). Therefore, we considered the types as follows: 

o Direct: Papers that explicitly mention the name of the 

type + debt. 

o Indirect: Determined from phrases in the text, such as 

technical debt derived from issues on the documentation 

(documentation debt). 

o General: We classified into general technical debt those 

papers that do not focus on a specific technical debt type 

directly or indirectly and consider only the concept of 

technical debt, such as technical debt management ap-

proach. 

• Indicators: Indicators are elements that help identify 

technical debt items (Alves et al., 2016). We created a 

list of the indicators cited by authors on each paper and 

its associated type of technical debt, based on the indica-

tors found by Alves et al. (2016). A new indicator was 

created when these previous indicators did not fit what 

was mentioned in a paper. We also collected data on how 

these indicators were empirically tested, including the 

artifact in which they are identified and data sources. 

• Management strategies: We extracted the management 

strategies described in each included paper. We used the 

same criteria as Alves et al. (2016): to be considered a 

management strategy, it must support the decision-mak-

ing about technical debt items. This definition includes 

activities for measuring, prioritizing, preventing, moni-

toring, documenting, and paying the debt. Each strategy 

and its definitions were collected as mentioned in each 

paper. 

• Evaluation studies: Evaluations are needed to deter-

mine the feasibility of the proposed strategies. There are 

several types of evaluation studies. We classified them 

into case studies, controlled experiments, or ethno-

graphic studies with the same criteria as in the previous 

study (Alves et al., 2016). Also, we documented the ar-

tifact considered, the programming language used, and 

the data sources used on each paper that performed an 

empirical evaluation.  

• Visualization techniques: Several visualization tech-

niques help understand the potential problems of tech-

nical debt in software projects. We extracted the visuali-

zation techniques described in the included papers for 

technical debt identification or management mentioned in 

each paper as Alves et al. (2016). 
 

The aforementioned research method is based on the pro-

cedure performed by Alves et al. (2016) in their study. In this 

paper, we aim to answer the same research questions by ap-

plying the same study selection and synthesis methods. How-

ever, this update’s protocol has two main differences from 

the original study methodology. One is the time-frame delim-

itation. We only considered publications between 2015 and 

2022, a restriction that was added to the search strategy cri-

teria. Moreover, we provide the definition of “general” tech-

nical debt classification. In the previous study, the authors 

refer to “type not specified” while we classify these papers as 

“general” technical debt to provide more clarity to the reader. 

However, we refer to the same type of papers (as described 

in the Synthesis Methods).  

Table 2. Data collection variables and their purpose. 

Data collection variable Purpose 

Title 

Demographic 

characterization 

Author 

Type of publication  

(workshop, conference, journal) 

Year of publication 

Digital library (database) 

Research topic (identification or man-

agement) 

Technical debt type RQ1 

Indicators RQ2 

Artifact considered (identification 

studies) 
RQ2 

Data source (identification studies) RQ2 

Management strategy (management 

studies) 
RQ3 

Evaluation type (if applicable: case 

studies, controlled experiments, eth-

nographic studies, action research) 

RQ2 and RQ3 

Visualization techniques RQ2 and RQ3 

4 Results 

This section presents the integration of our results and 

those obtained by Alves et al. (2016), which included 100 pa-

pers published between 2010 and 2014. Our study analyzes 

117 additional articles dating from 2015 to 2022 (see Appen-

dix A1). Figure 1 shows the number of studies included by 

publication type and year.  



 

Identification and Management of Technical Debt: A Systematic Study Update Murillo et al. 2023 

Figure 1. Number of studies by year and publication type. 

We searched the same databases using the same search string 

and applied the established selection criteria. Papers were 

published in symposia, journals, magazines, workshops, and 

conferences. From 2010 to 2014, workshops and conferences 

were the most common publication types. Between 2015 and 

2022, the most common publication types were conferences 

and journals. The decrease in publication on workshops and 

the rise of conferences shows that the theme has developed 

certain maturity over the years.  

The number of publications on technical debt identifica-

tion and management has been irregular during the last dec-

ade. Overall, the number of articles included in conferences 

has been rising. In 2010 only two papers were from confer-

ences, while this number increased to 17 in 2019. However, 

there may have been an impact on the publications done in 

2020 and 2021 due to the coronavirus pandemic. 

In this study, we performed the search on ACM Digital 

Library, IEEE Xplore, Science Direct, Springer Link, Engi-

neering Village, Scopus, Citeseer, and DBLP. Overall, since 

2010 most papers have been published in the IEEE Xplore 

and ACM Digital Library. However, the number of papers 

on Springer Link has increased considerably since 2015. 

Figure 2 shows the number of studies by digital library. 

4.1 Technical debt types (RQ1) 

Alves et al. (2016) proposed a taxonomy of technical debt 

that includes: design, architecture, documentation, test, 

code, defect, requirements, infrastructure, people, test auto-

mation, process, build, service, usability, and versioning 

debts.  

From 2010 to 2014, the most common technical debt 

types studied in the literature were design, architecture, and 

documentation. Also, a high concentration of studies ad-

dressed the test, code, and defect debt.  

Between 2015 and 2022, 77 studies did not focus on a 

particular type but addressed the topic in a general manner. 

In contrast, other papers focused on a specific technical debt 

type, such as code, design, or architecture. Consequently, 

technical debt is increasingly studied with a holistic ap-

proach rather than distinct kinds of debts that need to be 

managed differently. Figure 3 shows the number of papers 

included by type of technical debt. 

Of the 117 included papers (2015 - 2022), 34 addressed 

self-admitted technical debt, a concept commonly men-

tioned in the literature. Self-admitted technical debt (SATD) 

refers to situations in which developers are aware and admit 

that technical debt has been incurred. These scenarios are 

different from those with no consciousness that debt is pre-

sent.  

When SATD exists, issues may correspond to various tech-

nical debt types, such as code, architecture, documentation, 

etc. For this reason, those papers that addressed SATD were 

classified into the general technical debt (TD) category. 

 

Figure 2. Number of included papers by digital library. 

 

 
    

 

  
   

 

 

  

  

 

 

 

  

  

  
   

 

   

 

 

 

 

 

 

 

 

  
 

  

 

 
 

 

 

 

 

  

  

  

  

  

                                                    

 
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b
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o
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 orkshop Conference  ournal  ymposium Magazine

  

  

 

 

 

 

  

  

  

 

         

    

 CM  igital  ibrary

 pringer  ink

 copus

 cience  irect

Citeseer

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 umber of included papers

 
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     -          -     



 

Identification and Management of Technical Debt: A Systematic Study Update Murillo et al. 2023 

 

Figure 3. Number of included papers by technical debt type and 

year. 

Forty out of the 117 papers addressed a specific technical 

debt type, as described by (Alves et al., 2016). From 2015 to 

2022, architecture, code, and design debt were the most com-

mon types. However, there has been a significant reduction in 

studies focused on these types over the last seven years. For 

example, there was a reduction of nearly half of the publica-

tions on architecture and code debts, while design debt went 

from 42 publications to only five compared to the previous 

period (2010 – 2014).  

We found no articles about documentation, people, build, 

services, or usability debt during the last seven years. These 

types of technical debt have not been as extensively studied 

compared to others, so they represent potential areas for fur-

ther investigation in future work.  

As a result of the literature review, we did find two new 

types mentioned in the literature: security and elasticity 

debts. Security debt refers to security issues in the software, 

such as vulnerabilities or exploitable weaknesses (Izurieta et 

al., 2018). Elasticity debt is a concept that describes non-ef-

fective or non-efficient resource provisioning resulting from 

the lack of dynamical adaptation to resource consumption 

(Mera-Gómez et al., 2016). These two types of technical 

debt have been mentioned in few studies. Consequently, they 

cannot still be considered widely accepted types of technical 

debt. Both may be subtypes of requirement (security) and in-

frastructure (elasticity) papers and classified them as such 

when performing the literature review. 

4.2 Technical debt identification (RQ2) 

An essential step for technical debt management is its 

identification. Identification comprises activities or actions 

to detect the presence of debt in software artifacts. Out of the 

117 included papers, 47 addressed technical debt identifica-

tion. We extracted the indicators and type of technical debt 

associated with each paper. Indicators are symptoms that 

help identify technical debt items (Alves et al., 2016).  

From 2010 to 2014, forty-five indicators were found and 

presented by Alves et al. (2016). In this study, we found 11 

indicators mentioned in the literature between 2015 and 

2022. Table 3 shows the eleven indicators and the top 5 most 

common indicators presented previously (Alves et al., 2016): 

code smells, documentation issues, software architecture is-

sues, violation of modularity, and automatic static analysis 

issues. These indicators were either just mentioned or ana-

lyzed in the included papers. 

The results show significant differences between both 

periods. Code smells were the most common indicator in 

previous years, while the comments and commits were 

mostly mentioned during the last seven years. This fact is 

due to the considerable number of papers (34 in total) that 

addressed self-admitted technical debt in recent years, which 

used several strategies to analyze comments or commits to 

identify different types of technical debt, not only those re-

lated to source code. This represents a more holistic view, in 

which not only code issues are intended to be identified. 

Authors have recently studied SATD through natural lan-

guage processing, neural networks, deep learning, and ma-

chine learning. SATD may be identified using these different 

approaches on commits, comments, and issue trackers to be 

further prioritized and managed.  

4.2.1 Evaluation studies  

Most studies on technical debt identification have per-

formed empirical evaluations through case studies in recent 

years. However, there has been an increase in this type of 

study during the last seven years. A possible explanation for 

this is that the knowledge consolidated before 2015 gave the 

necessary foundations to perform empirical evaluations, 

such as case studies. The execution of case studies helps pro-

vide more information about the context in which the differ-

ent identification strategies are applicable. The growth in the 

number of case studies is relevant because it is vital to have 

multiple sources of empirical data to generalize results.  

We also found a significant increase in the number of 

controlled experiments. Figure 4 shows the number of em-

pirical evaluations performed by the number of papers and 

year of publication. 

 

Figure 4. Empirical evaluations on technical debt identification 
studies. 

 

 

 

 

 

  

  

 

  

 

 

 

 

 

 

 

 

  

  

  

  

  

  

  

      

 ersioning

 uild

 ervice

 sability

 eople

 rocess

 nfrastructure

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Test

 ocumentation

Code

 rchitecture

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 umber of studies

T
e
c
h
n
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a
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d
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b
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ty
p
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     -          -     

  

  

  

 

 

       

Case studies

Controlled e periment

 thnographic study

 umber of papers 

T
y
p
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f 
s
tu
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Identification and Management of Technical Debt: A Systematic Study Update Murillo et al. 2023 

Table 3. Indicators organized by technical debt (TD) type and period. 

Indicator 
2010 - 2014 2015 - 2022 

# Papers Technical debt types # Papers Technical debt types 

Code smells 52 Code, Design 1 General TD, Architecture 

Documentation issues 17 Documentation  - - 

Software architecture issues 9 Architecture 9 Architecture 

Violation of modularity 9 Architecture - - 

Automatic static analysis issues 9 Code, Design 3 Architecture, Code, General TD 

Comments  1 Documentation  26 Code, Requirements, General TD 

Uncorrected known defects 6 Defect, Test 1 General TD  

Immature software - - 1 General TD 

Feature usage and maintenance costs - - 1 General TD 

Insufficient resource provisioning - - 1 Infrastructure 

Low external/internal quality 1 Design 1 General TD 

Software design issues 4 Design 1 Design 

4.2.2 Artifacts and data sources  

We extracted the data source and artifact considered in each 

paper that performed an empirical evaluation. Figure 5 

shows the number of studies by artifact. From 2010 to 2014, 

the most common artifact was source code. The obtained re-

sults show that source code remains the primary artifact used 

to perform empirical evaluations; this may be because static 

analysis tools can help for these purposes. However, the 

number of studies considering source code decreased from 

58 between 2010 and 2014 to 39 from 2015 to 2022. 

 

Figure 5. Number of studies by artifact considered for technical 

debt identification. 

Researchers have started investigating by mining the re-

positories to extract metadata about technical debt in recent 

years. Alves et al. (2016) identified four different data 

sources: CMS (Configuration Management Systems), soft-

ware repositories, and bug tracking. The CMS were the most 

used in that period. In contrast, we found six different data 

sources from 2015 to 2020. Software repositories predomi-

nated, which makes sense since the most common artifact 

was source code. Figure 6 shows the number of papers by 

the data source used. 

4.2.3. Visualization techniques 

Only two papers on technical debt identification mentioned 

a visualization technique. The proposed methods are not ma-

ture because there is not much validation. Therefore, the vis-

ualization of technical debt is still an area that requires fur-

ther investigation. The proposed visualization techniques 

were the assessment graph (Shapochka & Omelayenko, 

2016) and coupling probability matrix (L. Xiao et al., 2016). 

 

Figure 6. Number of papers by the data source for technical debt 

identification. 

  

 

 

 

 

 

 

 

 

 

  

 

 

 

 

 

       

 ource code

 ocumentation

Test report

 ugs report

 rchitecture specification

 acklogs

Commit

Change report

 mplemented tests

 e uirement specification

 ervice utilization

 atabase data

 umber of studies

 
rt
if
a
c
t 
c
o
n
si
d
e
re
d

     -        -    

 

 

  

 

 

 

 

 

  

       

 ug tracking and CM 

 orkload simulation

 ug tracking

 nterview

 ot specified

CM 

 oftware repositories

 umber of papers

 
a
ta
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o
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     -          -     



 

Identification and Management of Technical Debt: A Systematic Study Update Murillo et al. 2023 

4.3 Technical debt management (RQ3) 

The management of technical debt includes several activities 

to control debt during the software development process. 

These activities aim to avoid bankruptcy situations in which 

the debt becomes uncontrollable.  

Out of the 117 included papers, 70 addressed technical 

debt management. This section presents the strategies pro-

posed by authors in the literature, the evaluation studies per-

formed, and the visualization techniques mentioned.  

4.3.1 Strategies for managing technical debt  

The first step for technical debt management is to identify its 

presence. Then, several strategies could be used for its timely 

administration to reduce interests’ impact. Table 4 shows the 

complete list of management strategies found during the lit-

erature review. The top 5 most studied strategies between 

2015 and 2022 were the following: 

• Automated analysis of code issues: Recent studies 

mention several tools to aid TD management: So-

nargraph for analyzing software architecture (von 

Zitzewitz, 2019), Teamscale to analyze software quality 

based on data from version control systems, issue track-

ers and other tools (Haas et al., 2019), Sonarqube to an-

alyze code and get several code metrics (Baldassarre et 

al., 2020), and CodeScene to perform behavioral code 

analysis, which can be helpful for debt prioritization 

and communication with stakeholders (Tornhill, 2018). 

These papers report code, architecture, test, and general 

technical debt management supported by tools that au-

tomatically identify code issues. The generated metrics 

or reports may be used by developers and stakeholders 

to prioritize, monitor, and perform the necessary man-

agement actions. One of the advantages of such tools is 

that they need minor human intervention to measure 

several code issues while creating awareness that TD 

exists. 

• Calculation of technical debt (TD) interest: Interest 

is the additional cost that will have to be assumed be-

cause of the delayed payment. Authors have proposed 

methods for its calculation to prioritize technical debt 

items according to the interest that will have to be as-

sumed (Chatzigeorgiou et al., 2015; Falessi & Reichel, 

2015). This allows decision-making about the appropri-

ate moment to pay the debt, depending on the accepta-

ble costs of each scenario. 

• Portfolio approach: In finance, the portfolio comprises 

the assets that an investor has. Portfolio management is 

carried out to decide what investments to make with the 

assets considering risks and return of investment. A TD 

portfolio approach brings financial concepts to TD and 

considers it a potential investment, whose final goal is 

to get more gains than losses (Guo & Seaman, 2011). 

The TD portfolio approaches are based on the financial 

portfolio theory and consider principal, interest, or cor-

relations with other TD items to help decision-making. 

Authors proposed a glossary of financial technical debt 

concepts (Akbarinasaji & Bener, 2016), while others 

present frameworks that consider portfolio theory 

(Nielsen & Skaarup, 2021; Rindell et al., 2019). Papers 

that consider more than only interest calculation and 

reference the portfolio theory were classified into port-

folio approaches rather than just interest calculation, 

while those that only mention interest formulas were 

classified as calculation of TD interest. 

• Prioritization approach: Authors suggest different 

methods to prioritize TD items. The purpose of prioriti-

zation is to determine the order in which technical debt 

will be paid. The proposed strategies include code 

smells ranking through automated tools (Alfayez & 

Boehm, 2019; Vidal et al., 2016), backlogs managed 

considering risks and business needs (Besker et al., 

2019), and approaches that focus on the business per-

spective (Stochel et al., 2020). 

• SATD removal approach: Authors have also sug-

gested management strategies for self-admitted tech-

nical debt removal. For example, natural language pro-

cessing could analyze source code comments and later 

compare their evolution among different versions of 

each file (da Maldonado et al., 2017). It is also possible 

to use deep neural networks to provide recommenda-

tions for SATD removal (Zampetti et al., 2020).  

Some of the included papers addressed strategies or tech-

niques identified in previous years (Alves et al., 2016). 

These proposals are the portfolio approach, options analysis, 

calculation of the principal and interest, and TD management 

in database schemas. However, the number of empirical 

evaluations performed on each strategy is still small. Over-

all, the authors have proposed their own strategies and tested 

them empirically instead of validating or comparing them to 

previous proposals. 

4.3.2 Evaluation studies  

Case studies have been the most frequent type of empirical 

evaluation performed on technical debt management. This is 

true for both periods, as shown in Figure 7. Nevertheless, 

the number of this type of study raised to more than double 

from 2015 to 2022. Also, ten papers presented action re-

search and controlled experiments in recent years, adding 

some diversity to the type of evaluation studies. 

From 2010 to 2014, few empirical studies were per-

formed in real settings. In contrast, subsequent years show 

more case studies and action research in real settings. The 

number of these evaluations is still small for every manage-

ment strategy. Still, it is essential to highlight that research-

ers have started to acknowledge the need for empirical test-

ing. 

 
Figure 7. Number of papers by type of study. 

 

 

  
  

       

Controlled e periment

 ction research

Case studies

 umber of papers 

T
y
p
e
 o
f 
st
u
d
y

     -         -     



 

Identification and Management of Technical Debt: A Systematic Study Update Murillo et al. 2023 

4.3.3. Visualization techniques 

Only four papers on technical debt management mentioned 

visualizations techniques, which were the following: dy-

namic graphic (Pacheco et al., 2018), line chart (Falessi & 

Reichel, 2015), portfolio matrix (Plösch et al., 2018), and 

probabilistic cause-effect diagrams (Rios et al., 2019). Each 

technique was only mentioned once. Therefore, they still re-

quire further research to determine their applicability. 

5 Discussion 

This paper studied the technical debt identification and man-

agement research landscape from 2015 to 2022 and inte-

grated our results with previous investigation efforts that an-

alyzed the period 2010 - 2014 (Alves et al., 2016). This sec-

tion presents a discussion of the obtained results. 

5.1 Technical debt types (RQ1) 

Technical debt as an analogy with financial debt is well 

known among authors in the academic literature. Overall, 

there is a common understanding of the technical debt con-

cept itself as taking shortcuts during software development, 

leading to several future costs. However, different technical 

debt classifications exist, and there is no clarity on which are 

the accepted types.  

Alves et al. (2016) proposed a taxonomy that includes: 

design, architecture, documentation, test, code, defect, re-

quirements, infrastructure, people, test automation, process, 

build, service, usability, and versioning debts. Still, other 

studies mention different classifications, but there is a lack 

of consensus on some technical debt types. 

To the best of our knowledge, besides the proposal of 

Alves et al. (2016), only three other papers address technical 

debt types or propose a classification (Li et al., 2015; Rios et 

al., 2018; Tom et al., 2013). One of these classifications was 

presented as a result of a non-academic literature review and 

interviews with people in the software development industry 

(Tom et al., 2013). Others were derived from a systematic 

mapping and a tertiary study of academic literature (Alves et 

al., 2016; Rios et al., 2018).  

Some types of technical debt are presented in the three 

studies: code, design, architecture, and test debt. In fact, we 

found that from 2015 to 2022, the most addressed types cor-

respond to design, architecture, code, and test debts. We ob-

served that authors use these terms consistently, agreeing 

with their general meaning. Therefore, these particular types 

may be considered accepted technical debt types. On the 

other hand, the concept of self-admitted technical debt 

(SATD) is overall consistent among papers and referred to as 

a technical debt type.  

Other types are much less established in the literature. 

For example, between 2010 and 2022, process and people 

debts were only mentioned in three papers each, while usa-

bility, service, build, and versioning debts were only cited in 

two papers each. There is also another new concept men-

tioned in the literature: variability debt. It was not identified 

through the performed review because papers mentioning it 

do not meet the acceptance criteria proposed in this study. 

However, it may be considered for future research. Variabil-

ity debt refers to software’s characteristics that allow it to 

adapt (create variants) for different needs (Wolfart et al., 

2021). These concepts are still not widely accepted since not 

much literature is available on them. In some cases, they may 

not even represent technical debt categories themselves but 

subcategories. The same may be true for security and elas-

ticity debts, which could be subcategories of other types of 

debt. Another relevant aspect is a position in the literature 

that considers defects and processes as non-technical debt 

(Li et al., 2015). However, this does not imply that the ele-

ments addressed by these types of technical debt lack im-

portance. 

Figure 8 presents a heatmap showing the number of pub-

lications by technical debt type and year, including papers 

from 2010 to 2022. The lack of clarity on some technical 

debt types and the number of existing categories may have 

influenced the authors’ choice of categorizing their work. 

Still, the number of papers that presented typifications of 

technical debt dropped in 2016. Authors may have inadvert-

ently reached the consensus that technical debt is an issue to 

be managed without necessarily specifying its type. There 

was a turning point between 2014 and 2015, in which authors 

left aside the classification and began to focus their studies 

on technical debt management. 

5.2 Technical debt identification (RQ2) 

Technical debt identification comprises actions to detect debt 

presence; it is the first step necessary for its management. In 

recent years, source code has been the most common artifact 

that helps technical debt identification since it is possible to 

implement several techniques, algorithms, or tools to detect 

debt automatically. However, other artifacts may be used, 

such as test cases.  

Figure 9 summarizes the findings on technical debt iden-

tification between 2010 and 2022. When technical debt ex-

ists, there are several indicators that show symptoms of its 

presence. Identification approaches help find indicators 

through several artifacts and data sources for further manage-

ment.  

Comments on source code were the most common indi-

cator from 2015 to 2022. Analyzing comments helps to iden-

tify self-admitted technical debt. The increasing number of 

studies on SATD suggests that there is valuable information 

that developers themselves can provide through comments. 

Nevertheless, it is a future research opportunity to explore 

how to take advantage of developers’ knowledge of the code 

issues in other ways, different from only comments or com-

mit messages.  

 



 

Identification and Management of Technical Debt: A Systematic Study Update Murillo et al. 2023 

 

Figure 8. Heatmap showing the number of publications by technical debt type and year. 

The automatic detection of technical debt allows getting 

a variety of quantitative measurements. Additionally, organ-

izations may be interested in also having qualitative 

measures on technical debt, which has not been much ex-

plored in the literature and constitutes a future work oppor-

tunity.  epending on every project’s business objectives and 

needs, organizations may identify those measurements that 

may help them further manage technical debt in their con-

texts.  

In the academic literature, the number of empirical eval-

uations on technical debt identification has increased in re-

cent years, which is beneficial for both researchers and prac-

titioners because they help discover the indicators’ applica-

bility in different contexts. However, research has concen-

trated on identification based on source code, while other ar-

tifacts and data sources may be further investigated. 

Few studies proposed visualization techniques for tech-

nical debt identification between 2010 and 2022. This is still 

an open issue and research opportunity. Technical debt visu-

alization is important because it may support communication 

between developers and stakeholders while aiding decision-

making on further technical debt management and preven-

tion. 

5.3 Technical debt management (RQ3) 

Technical debt management comprises actions or activities 

performed to control debt once it has been identified. Authors 

in the literature have proposed several strategies for debt 

management. In general, papers present new proposals and 

test them empirically instead of testing others previously de-

scribed in the literature. However, the number of empirical 

evaluations, especially case studies, has increased during the 

last seven years, along with the number of proposals. Table 4 

shows the complete list of strategies found in this replication 

study. 

From 2015 to 2022, many strategies proposed for tech-

nical debt management were supported by automatic tools 

applied to source code, such as Sonargraph, CodeScene, So-

narqube, and Teamscale  (Baldassarre et al., 2020; Haas et 

al., 2019; Tornhill & Ab, 2018; von Zitzewitz, 2019). The 

obtained measurements through such tools help to prioritize 

and support decision-making. Other authors compared pen-

alties and gamification techniques for technical debt using 

automated tools in educational contexts, showing that re-

wards may be a suitable option for TD management (Crespo 

et al., 2021). 

Other papers presented novel frameworks or models for 

managing technical debt during the software development 

process with a more holistic perspective, including several 

process elements or phases. For example, some authors pro-

pose creating a guild, a group of people that help address TD 

management and guide its payment (Detofeno et al., 2021). 

Moreover, another paper mentioned encouraging and re-

warding incentives for developers to manage technical debt 

(Besker et al., 2022). Other authors evaluate a business pri-

oritization approach that allows an alignment between busi-

ness and technical stakeholders for prioritizing TD items 

(Reboucas De Almeida, 2019), while additional research ef-

forts report using TD tickets that allow TD management and 

prevention (Wiese et al., 2021). Nevertheless, few papers 

specifically address the human resources involved during 

software development, which is essential because it is known 

that people issues can also lead to technical debt (Rios et al., 

2020). 

Between 2010 and 2014, twenty-two papers on technical 

debt management described a visualization technique. In 

contrast, we only found four visualization techniques in pa-

pers about technical debt management from 2015 to 2022. 

This shows a significant decrease in research efforts, even 

when only a few studies address this topic. 

Although it is not part of the research questions of this 

paper, it is worth mentioning that there are different perspec-

tives regarding the definition of TD management in the 



 

Identification and Management of Technical Debt: A Systematic Study Update Murillo et al. 2023 

literature. In this paper, we used the same definition of TD 

management as Alves et al. (2016) to be conceptually con-

sistent. However, some authors consider that TD manage-

ment includes its recognition, analysis, monitoring, and 

measurement (Izurieta et al., 2016), while others consider its 

identification, assessment, and remediation (Griffith et al., 

2014). Furthermore, Li et al. (2015) present nine activities 

for technical debt management: identification, measurement, 

prioritization, prevention, monitoring, repayment, documen-

tation, and communication.  

As several definitions of TD management suggest, there 

are plenty of actions that help to control debt during software 

development. However, the concepts mentioned by the dif-

ferent authors agree that the first step for managing TD is to 

identify or recognize the presence of debt and start measur-

ing (quantifying) it. These two activities alone are not the 

solution for TD issues. Subsequent strategies are needed to 

take effective actions toward TD management. It may be 

necessary to prioritize, monitor, repay, and document debt 

(Li et al., 2015). Prioritization includes deciding the order of 

importance or urgency to pay debt items. Monitoring refers 

to supervising several aspects related to TD, such as histori-

cal costs and resolution times. It is not possible to monitor 

debt if metrics have not been established and measured. 

Also, the progress on debt issues cannot be tracked if there 

is no monitoring. Moreover, debt repayment or remediation 

is the resolution of a TD item. Also, documentation and com-

munication with stakeholders may be needed. Lastly, organ-

izations may be interested in establishing prevention actions.  

Technical debt is a context-dependent issue (Fernández-

Sánchez et al., 2017). Therefore, the context must be well 

understood to take appropriate actions for debt management. 

Gathering and analyzing data (not only about TD) may be 

useful for establishing a TD management plan. For example, 

debt management may be different in an agile organization 

than in a traditional one. Also, the team size and type of soft-

ware that is developed are variables that may be considered. 

Moreover, determining the main debt issues perceived by the 

software developers could be a starting point. Regardless of 

which definition of TD management is used, the appropriate 

strategies will depend solely on the specific needs, issues, 

and objectives of the organizational context. Furthermore, 

the selected strategies may vary or be adapted in time de-

pending on the obtained outcomes. 

The following sections present the threats to the validity 

and conclusions of this paper. 

 

Figure 9. Technical debt identification concept map. 

  

              

          

          

 rtifacts  ata sources

 ource code

 ocumentation

 acklogs

Tests

 oftware repositories

CM 

 ug tracking

 tatic  nalysis

 atural  anguage  rocessing

Machine  earning

 nterview    urvey

Manifests through

 ound using

 upported by

                         

 ncluding

 epresented using

                        

 ssessment graph

Coupling probability matri 

 lags in code

   maps

 catterplot

Correlation matri 

Time range

Timeline

Tree map

 ncluding



 

Identification and Management of Technical Debt: A Systematic Study Update Murillo et al. 2023 

Table 4. Management strategies proposed in the academic literature from 2015 to 2022. 

Strategy proposed 
Number 

of papers 
 References 

Automated analysis of code issues 7 
(Anderson et al., 2019; Baldassarre et al., 2020; Fontana et al., 2016; Haas et al., 

2019; Lahti et al., 2021; Sharma, 2019; Tornhill & Ab, n.d.; von Zitzewitz, 2019) 

Calculation of TD interest 6 

(Ampatzoglou, Ampatzoglou, Avgeriou, et al., 2015; Ampatzoglou et al., 2018; 

Chatzigeorgiou et al., 2015; Falessi & Reichel, 2015; Kontsevoi et al., 2019; Martini 

& Bosch, 2016, 2017a) 

Portfolio approach 5 
(Akbarinasaji & Bener, 2016; Aldaeej & Seaman, 2018; Nielsen & Skaarup, 2021; 

Plösch et al., 2018; Rindell et al., 2019) 

Prioritization approach 5 
(Alfayez & Boehm, 2019; Besker et al., 2019; de Lima et al., 2022; Stochel et al., 

2020; Vidal et al., 2016) 

SATD removal approach 3 (da Maldonado et al., 2017; T. Xiao et al., 2021; Zampetti et al., 2020) 

Approach for technical debt decision mak-

ing 
3 (Codabux & Williams, 2016; Pacheco et al., 2018; Ribeiro et al., 2017) 

Model for TD alignment with business 3 (Reboucas De Almeida, 2019; Reboucas De Almeida et al., 2018, 2019) 

Calculation of TD principal 3 (Akbarinasaji et al., 2016; Kontsevoi et al., 2019; Kosti et al., 2017) 

Process framework for managing TD 2 (Oliveira et al., 2015; Ramasubbu & Kemerer, 2019) 

Model for optimizing technical debt 2 (Perez et al., 2019; Yli-Huumo et al., 2016) 

Strategic TD management model 2 (Ciancarini & Russo, 2020; Martini et al., 2016) 

Framework for TD management 2 (Borup et al., 2021; Wiese et al., 2021) 

Continuous Architecting Framework for 

Embedded Software and Agile (CAFFEA) 
1 (Martini & Bosch, 2017b) 

Automated identification of refactoring 

candidates 
1 (Tornhill, 2018) 

Automated refactoring 1 (Mohan et al., 2016) 

Automatic identification and interactive 

monitoring 
1 (Fernandez-Sanchez et al., 2017) 

Benchmarking-based model 1 (Mera-Gómez et al., 2016) 

Continuous/extensive testing 1 (Trumler & Paulisch, 2016) 

Estimation approach 1 (Lenarduzzi et al., 2019) 

Linear-predictive lifecycle/incremental-

predictive lifecycle application 
1 (Fairley & Willshire, 2017) 

Maintainability Model 1 (di Biase et al., 2019) 

Managing TD in database schemas 1 (Albarak et al., 2020) 

Metric for managing architectural tech-

nical debt 
1 (Kouros et al., 2019) 

Model-driven development (preemptive) 1 (Izurieta et al., 2015) 

Model of maintenance cost growth 1 (Snipes & Ramaswamy, 2018) 

Propagation model 1 (Holvitie et al., 2016) 

Real options analysis 1 (Abad & Ruhe, 2015) 

TD enhanced backlog 1 (Martini, 2018) 

Visual Thinking 1 (Chicote, 2017) 

TD cause-effect analysis 1 (Rios et al., 2019) 

Normative Process Framework 1 (de Leon-Sigg et al., 2020) 

TD predictive model 1 (Aversano et al., 2021) 

Conceptual model for holistic debt man-

agement 
1 (Malakuti & Heuschkel, 2021) 

Automated identification of deprecation in 

metamodels 
1 (Iovino et al., 2020) 

TD Management Guild 1 (Detofeno et al., 2021) 

Encouraging and rewarding incentives 1 (Besker et al., 2022) 



 

Identification and Management of Technical Debt: A Systematic Study Update Murillo et al. 2023 

6 Threats to validity 

The results presented in this systematic mapping may have 

been affected by the following threats to validity: 

• Publication bias: Relevant studies may not have been 

returned when performing the literature search. Sev-

eral databases were consulted, and we did a backward 

snowballing process to find as many studies as possi-

ble to address this threat.  

• Search string: It is possible that some papers in the 

literature propose a way to manage or identify tech-

nical debt but do not explicitly mention that they are 

suggesting an approach for technical debt. Therefore, 

these papers may have been left out when performing 

the search. Still, our focus was on literature regarding 

technical debt strategies. 

Also, since this was an update to a previous systematic map-

ping study, other limitations and threats to validity include: 

• Consistency in integrating the results: This paper up-

dates the previous work by Alves et al. (2016). Differ-

ent researchers performed the data extraction than the 

original study, and we cannot ensure that this could 

cause some differences in the updated results. However, 

our research method is based on the procedure per-

formed by Alves et al. (2016) in their study. Also, two 

of the original authors contributed to the elaboration of 

this paper and reviewed the obtained results from the 

data extraction to ensure there was no misunderstanding 

of concepts between the two sets of primary sources to 

address this risk.  

Lastly, we performed the paper selection process in March 

2022, so the results of that year are not fully complete. These 

aspects are the limitations of this study. 

7 Conclusion 

This paper explored the evolution of technical debt iden-

tification and management research landscape over a decade. 

We searched for studies on eight databases and analyzed ac-

ademic literature published between 2015 and 2022. By ap-

plying the defined search string and the inclusion criteria, we 

found 117 papers. We integrated our results with a previous 

study (Alves et al., 2016) that analyzed literature from 2010 

to 2014.  

In addition to the technical debt types mentioned in the 

taxonomy by Alves et al. (2016), there are three new terms 

in the literature: security, elasticity, and variability debt. The 

security type refers to security issues in the software, such as 

vulnerabilities or exploitable weaknesses (Izurieta et al., 

2018). On the other hand, elasticity debt is a concept that re-

fers to non-effective or non-efficient resource provisioning 

resulting from the lack of dynamical adaptation to resource 

consumption (Mera-Gómez et al., 2016).  Lastly, variability 

debt comprises the lack of software characteristics that allow 

it to adapt (create variants) for different needs (Wolfart et al., 

2021). 

Unlike the previous mapping, most of the included papers 

addressed technical debt without focusing on specific types. 

This shows that the technical debt phenomenon is analyzed 

more holistically. Still, those papers that focused on specific 

types of technical debt studied those identifiable or measur-

able through code. The most frequent artifacts and data 

sources are the source code and repositories; this may be be-

cause there are various code and repository data analysis 

tools. There are no such abundant tools for analyzing other 

types of debts, like documentation, people, and infrastruc-

ture. 

Over the years, several proposals have been developed 

for technical debt management. However, as in the previous 

systematic mapping, there is a need for more research to val-

idate the effectiveness of the proposals and their applicability 

in different contexts. Another finding was that only a few 

studies included in the update proposed a visualization strat-

egy. Therefore, the topic of technical debt visualization con-

tinues to be a future research opportunity. 

The automatic identification of debt through the analysis 

of comments, commits, and source code is among the main 

proposals found in the literature published between 2015 and 

2022. Several evaluations have been performed through case 

studies, controlled experiments, and action research. The 

number of evaluations has been rising through the years, 

which is particularly important for consolidating the 

knowledge gained in the research area. However, it is still 

required to perform more evaluations to generalize the ob-

tained results.  

The most relevant findings of this paper were the following: 

• Investigations on technical debt identification and man-

agement have increasingly changed their focus to a 

more holistic perspective, considering technical debt as 

a global problem during the software development pro-

cess instead of analyzing it as a set of different isolated 

problems. However, a significant number of investiga-

tions still focus on technical debt types closely related 

to source code.  

• The number of empirical evaluations performed on 

each strategy is still small. In most cases, authors have 

proposed their own strategies and tested them empiri-

cally instead of testing previous proposals. 

• Recent research on technical debt has focused on its 

management, while the proposal of new types has de-

creased dramatically since 2016. Creating new catego-

ries seems unnecessary, while authors may have inad-

vertently reached the consensus that technical debt is an 

issue to manage without specifying its type. 

• Overall, authors agree on the general meaning of code, 

design, architecture, and test debt, which suggests that 

these are widely accepted technical debt types.  

Likewise, future work possibilities include the following: 

• Research on how to use developers’ knowledge on ex-

isting technical debt, not only focused on their com-

ments or commit messages. It is an opportunity to 



 

Identification and Management of Technical Debt: A Systematic Study Update Murillo et al. 2023 

explore this knowledge as a valuable asset for technical 

debt identification. 

• Creating tools for analyzing certain types of debt, such 

as documentation, people, and infrastructure is a poten-

tial research opportunity since there is a lack of such 

tools. 

• There is still a small number of proposals regarding 

technical debt visualization; this is a future research op-

portunity, particularly considering that visualization 

techniques can help to better communicate with stake-

holders. 

• Few studies have explored strategies with a less-tech-

nical approach but focus on human resources, such as 

creating guilds, communities of practice, and rewards 

or incentives. Therefore, performing such investiga-

tions is a future opportunity. 

• There is a need to analyze which strategies are best in 

specific contexts (for example, public or private organ-

izations). 

The next steps in the research could be regarding how tech-

nical debt can be used as a competitive advantage, generat-

ing value rather than bringing undesired and costly conse-

quences. 

Acknowledgments 

The authors thank Dr. Carolyn Seaman for her valuable sug-

gestions and comments. This work was partially supported 

by CITIC at the University of Costa Rica. Grant No. 834-B4-

412. 

 Appendices 

A1. Complete list of included papers 

The complete bibliography of the 117 papers analyzed in the 

full-text review is available at:  

https://drive.google.com/file/d/1g8THUuny-

SVuhDWBr5A_ScVlTXWcCnta/view?usp=sharing 

 

A2. List of included papers about technical debt 

identification 

The complete list of included papers about technical debt 

identification and the artifact considered is available at; 

https://drive.google.com/file/d/1tXn8Sv6og_N59dhZKjKtc

MmAZcl4Ud3e/view?usp=sharing 

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