On the Relationship between Self-Admitted Technical Debt Removals and Technical Debt Measures

Iammarino, Martina; Carapella, Mimmo; Vecchio, Andrea Del; Nardi, Laura

doi:10.3390/a13070168

Cited by 9 publications

(4 citation statements)

References 21 publications

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“…They determined if the removal of the SATD resulted in lower objective measures of technical debt. According to the results, only in a few cases did the removal of SATD correspond to an effective reduction of technical debt values, while, in other cases, the indicated classes were removed [19]. However, we did not detect any research comparing the values of the SATD and the measured technical debt, which is the main research topic of this paper.…”

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confidence: 69%

“…Jie et al [18] stated that the developers who cause technical debt eliminate more than two-thirds of that debt. Aversano et al [19] researched the relationship between the SATD and the measured technical debt values. They determined if the removal of the SATD resulted in lower objective measures of technical debt.…”

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confidence: 99%

“…Therefore, only three methods are highlighted: the SQALE, the CAST, and the SIG. The SQALE method was used in 10 studies [5,6,8,[11][12][13][15][16][17]19], CAST in 3 studies [5,6,15], and SIG in 6 studies [5,6,10,13,15,17]. Each of the methods in Table 1 uses a different quality model.…”

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confidence: 99%

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The Gap between the Admitted and the Measured Technical Debt: An Empirical Study

et al. 2022

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Technical debt is a well understood and used concept in IT development. The metaphor, rooted in the financial world, captures the amount of work that development teams owe to a product. Every time developers take a shortcut within development, the technical debt accumulates. Technical debt identification can be accomplished via manual reporting on the technical debt items, which is called self-admitted technical debt. Several specialised methods and tools have also emerged that promise to measure the technical debt. Based on experience in the community, the impression emerged that the measured technical debt is of a significantly different amount than the self-admitted debt. In this context, we decided to perform empirical research on the possible gap between the two. We investigated 14 production-grade software products while determining the amount of accumulated technical debt via (a) a self-admitting procedure and (b) measuring the debt. The outcomes show clearly the significant difference in the technical debt reported by the two methods. We urge development and quality-assurance teams not to rely on technical debt measurement alone. The tools demonstrated their strength in identifying low-level code technical debt items that violate a set of predefined rules. However, developers should have additional insight into violations, based on the interconnected source code and its relation to the domain and higher-level design decisions.

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The Gap between the Admitted and the Measured Technical Debt: An Empirical Study

et al. 2022

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“…Actually, by monitoring the trends of maintainability metrics, we may determine in advance which parts of the codebase may likely be abandoned by developers and rewritten deemed non-maintainable [10]. Developers themselves may indicate explicitly introduction of technical debt (i.e., Self-Admitted Technical Debt), and its removal in many cases takes the form of rewriting the impacted code, rather than its correction [11]. However, we should avoid a trap of understanding technical debt only according to the scope presented by the tools, e.g., SonarQube [12].…”

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confidence: 99%

Technical Debt Prioritization in Telecommunication Applications: Why the Actual Refactoring Deviates from the Plan and How to Remediate It? Case Study in the COVID Era

Stochel

Wawrowski²,

Chołda

2022

Applied Sciences

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This paper focuses on application of a technical debt prioritisation technique in telecommunication software managing a fleet of devices for a video surveillance system. Technical debt for this application was gathered, categorised and prioritised according to the Continuous Debt Valuation Approach (CoDVA), previously proposed by the authors. The following research question was posed: Is prioritising technical debt reduction based on CoDVA effective (i.e., executed as per plan, bringing tangible benefits)? The outbreak of COVID-19 pandemic caused unprecedented disturbance to the engineering organisations worldwide, therefore the technical debt identification phase had to be adapted to cope with a switch to forced working-from-home mode. This was achieved by applying the Wisdom of Crowds method, ensuring broad participation of engineers, and providing a fairly complete picture of the accrued technical debt. Nevertheless, the actual technical debt reduction activities did not follow exactly the expected guidelines. The three main causes of this phenomenon were discovered: continuous refactoring approach, sizing of technical debt items, and the broadened scope of refactoring activities. Therefore, as a result of this case study we propose to adopt a specific broadened definition of technical debt and follow a few rules for defining its scope and granularity.

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Technical Debt Forecasting from Source Code Using Temporal Convolutional Networks

Bernardi

Cimitile

Iammarino

2022

Product-Focused Software Process Improvement

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On the Relationship between Self-Admitted Technical Debt Removals and Technical Debt Measures

Abstract: * Correspondence: aversano@unisannio [...]

Cited by 9 publications

References 21 publications

The Gap between the Admitted and the Measured Technical Debt: An Empirical Study

The Gap between the Admitted and the Measured Technical Debt: An Empirical Study

Technical Debt Prioritization in Telecommunication Applications: Why the Actual Refactoring Deviates from the Plan and How to Remediate It? Case Study in the COVID Era

Technical Debt Forecasting from Source Code Using Temporal Convolutional Networks

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