A tertiary study on technical debt: Types, management strategies, research trends, and base information for practitioners

Rios, Nicolli; Mendonça, Manoel; Spínola, Rodrigo O.

doi:10.1016/j.infsof.2018.05.010

Cited by 164 publications

(140 citation statements)

References 26 publications

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“…• We found that the business perspective can affect the prioritization of technical debt items; • We found that business processes can facilitate the communication and prioritization of technical debt items; • We extended the analysis model presented by Rios et al. [13] by adding the link between technical debt items and the business processes; • We extended the work by Guo and Seaman [2], [9] to propose a conceptual model to support technical debt management decisions while taking business process management into account. As part of future work, we aim to conduct surveys and interviews with other companies, to add to the results found in this study.…”

Section: Discussionmentioning

confidence: 83%

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Aligning Technical Debt Prioritization with Business Objectives: A Multiple-Case Study

Almeida

Kulesza

Feitosa³

et al. 2018

2018 IEEE International Conference on Software Maintenance and Evolution (ICSME)

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Section: Discussionmentioning

confidence: 83%

“…BP Elements compose the "Business Process", which also has its overall priority and criticality. This model extends the conceptual model presented by Rios et al [13] by adding the business process perspective.…”

Section: Case Studymentioning

confidence: 75%

Aligning Technical Debt Prioritization with Business Objectives: A Multiple-Case Study

Almeida

Kulesza

Feitosa³

et al. 2018

2018 IEEE International Conference on Software Maintenance and Evolution (ICSME)

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“…A considerable amount of literature has been published on TD. The current state of research on TD can be looked up in a tertiary study by [10] as well as in systematic mapping studies by [8] and [11].…”

Section: Related Workmentioning

confidence: 99%

Technical Debt in Data-Intensive Software Systems

Foidl

Felderer

Biffl

2019

2019 45th Euromicro Conference on Software Engineering and Advanced Applications (SEAA)

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The ever-increasing amount, variety as well as generation and processing speed of today's data pose a variety of new challenges for developing Data-Intensive Software Systems (DISS). As with developing other kinds of software systems, developing DISS is often done under severe pressure and strict schedules. Thus, developers of DISS often have to make technical compromises to meet business concerns. This position paper proposes a conceptual model that outlines where Technical Debt (TD) can emerge and proliferate within such data-centric systems by separating a DISS into three parts (Software Systems, Data Storage Systems and Data). Further, the paper illustrates the proliferation of Database Schema Smells as TD items within a relational database-centric software system based on two examples.

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“…Effects of TD can be noticed in different stages of software development due to different types of debt. Low quality, delivery delay, low maintainability, rework, and financial loss are among the top 10 most commonly impactful effects of TD [36,37].…”

Section: Introductionmentioning

confidence: 99%

“…Finally, Rios et al [36,37] identified, through a tertiary study, a list of situations in which debt items can be found in software projects, and organized a map representing the state of the art of activities, strategies and tools to support the TD management. According to the authors, there are a number of gaps that need to be addressed when dealing with TD management.…”

Section: Introductionmentioning

confidence: 99%

Understanding automated and human-based technical debt identification approaches-a two-phase study

Spínola

Zazworka²,

Vetrò

et al. 2019

J Braz Comput Soc

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Context: The technical debt (TD) concept inspires the development of useful methods and tools that support TD identification and management. However, there is a lack of evidence on how different TD identification tools could be complementary and, also, how human-based identification compares with them. Objective: To understand how to effectively elicit TD from humans, to investigate several types of tools for TD identification, and to understand the developers' point of view about TD indicators and items reported by tools. Method: We asked developers to identify TD items from a real software project. We also collected the output of three tools to automatically identify TD and compared the results in terms of their locations in the source code. Then, we collected developers' opinions on the identification process through a focus group. Results: Aggregation seems to be an appropriate way to combine TD reported by developers. The tools used cannot help in identifying many important TD types, so involving humans is necessary. Developers reported that the tools would help them to identify TD faster or more accurately and that project priorities and current development activities are important to be considered together, along with the values of principal and interest, when deciding to pay off a debt.Conclusion: This work contributes to the TD landscape, which depicts an understanding between different TD types and how they are best discovered.

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A tertiary study on technical debt: Types, management strategies, research trends, and base information for practitioners

Cited by 164 publications

References 26 publications

Aligning Technical Debt Prioritization with Business Objectives: A Multiple-Case Study

Aligning Technical Debt Prioritization with Business Objectives: A Multiple-Case Study

Technical Debt in Data-Intensive Software Systems

Understanding automated and human-based technical debt identification approaches-a two-phase study

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