Understanding the impact of technical debt in coding and testing

Abad, Zahra Shakeri Hossein; Karimpour, Reza; Ho, Jason; Didar-Al-Alam, S. M.; Ruhe, Guenther; Tse, Edward; Barabash, Kevin; Hargreaves, Ian

doi:10.1145/2897022.2897023

Cited by 2 publications

(2 citation statements)

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“…Several studies have examined the relationship between TD and software testing [30,34]. Some studies [31,32] investigated what constitutes adequate testing tools when focusing on TD.…”

Section: Related Workmentioning

confidence: 99%

“…"CodeScene" works well in practice for TD prioritization with a stable product version [32]. The authors in [34] hypothesized that there is no considerable difference between the percentage of existing TD and carry extra debt because they may produce results that denote only the local minima within the search algorithm. Test-case prioritization approaches order test cases, such that those with the highest priority according to a particular criterion (a "fitness metric") are executed first.…”

Section: Related Workmentioning

confidence: 99%

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Optimizing Regression Testing with AHP-TOPSIS Metric System for Effective Technical Debt Evaluation

Zarrad

Bahsoon

Manimaran

2023

Preprint

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Regression testing is essential to ensure that the actual software product confirms the expected requirements following modification. However, it can be costly and time-consuming. To address this issue, various approaches have been proposed for selecting test cases that provide adequate coverage of the modified software. Nonetheless, problems related to omitting and/or rerunning unnecessary test cases continue to pose challenges, particularly with regard to technical debt (TD) resulting from code coverage shortcomings and/or overtesting. In the case of testing-related shortcomings, incurring TD may result in cost and time savings in the short run, but it can lead to future maintenance and testing expenses. Most prior studies have treated test case selection as a single- or two-objective optimization problem. This study introduces a multi-objective decision-making (MODM) approach to quantify and evaluate TD in regression testing. The proposed approach combines the analytic-hierarchy-process (AHP) method and the technique of order preference by similarity to an ideal solution (TOPSIS) to select the most ideal test cases in terms of objective values defined by the test cost, code coverage, and test risk. This approach effectively manages the software regression testing problems. The AHP method was used to eliminate subjective bias when optimizing objective weights, while the TOPSIS method was employed to evaluate and select test-case alternatives based on TD. The effectiveness of this approach was compared to that of a specific multi-objective optimization method and a standard coverage methodology. Unlike other approaches, our proposed approach always accepts solutions based on balanced decisions by considering modifications and using risk analysis and testing costs against potential technical debt. The results demonstrate that our proposed approach reduces both TD and regression testing efforts. 2022 MSC: 00-01, 99-00

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“…Several studies have examined the relationship between TD and software testing [30,34]. Some studies [31,32] investigated what constitutes adequate testing tools when focusing on TD.…”

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%