Black-box Test Case Selection by Relating Code Changes with Previously Fixed Defects

Çıngıl, Tutku; Sözer, Hasan

doi:10.1145/3530019.3530023

The International Conference on Evaluation and Assessment in Software Engineering 2022 2022

DOI: 10.1145/3530019.3530023

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Black-box Test Case Selection by Relating Code Changes with Previously Fixed Defects

Tutku Çıngıl

Hasan Sözer

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Cited by 2 publications

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State of Practical Applicability of Regression Testing Research: A Live Systematic Literature Review

2023

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Context: Software regression testing refers to rerunning test cases after the system under test is modified, ascertaining that the changes have not (re-)introduced failures. Not all researchers’ approaches consider applicability and scalability concerns, and not many have produced an impact in practice. Objective: One goal is to investigate industrial relevance and applicability of proposed approaches. Another is providing a live review, open to continuous updates by the community. Method: A systematic review of regression testing studies that are clearly motivated by or validated against industrial relevance and applicability is conducted. It is complemented by follow-up surveys with authors of the selected papers and 23 practitioners. Results: A set of 79 primary studies published between 2016-2022 is collected and classified according to approaches and metrics. Aspects relative to their relevance and impact are discussed, also based on their authors’ feedback. All the data are made available from the live repository that accompanies the study. Conclusions: While widely motivated by industrial relevance and applicability, not many approaches are evaluated in industrial or large-scale open-source systems, and even fewer approaches have been adopted in practice. Some challenges hindering the implementation of relevant approaches are synthesized, also based on the practitioners’ feedback.

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State of Practical Applicability of Regression Testing Research: A Live Systematic Literature Review

2023

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Test Case Minimization with Quantum Annealers

Wang,

Muqeet,

Yue

et al. 2024

ACM Trans. Softw. Eng. Methodol.

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Quantum annealers are specialized quantum computers for solving combinatorial optimization problems with special quantum computing characteristics, e.g., superposition and entanglement. Theoretically, quantum annealers can outperform classic computers. However, current quantum annealers are constrained by a limited number of qubits and cannot demonstrate quantum advantages. Nonetheless, research is needed to develop novel mechanisms to formulate combinatorial optimization problems for quantum annealing (QA). However, QA applications in software engineering remain unexplored. Thus, we propose BootQA , the very first effort at solving test case minimization (TCM) problems on classical software with QA. We provide a novel TCM formulation for QA and utilize bootstrap sampling to optimize the qubit usage. We also implemented our TCM formulation in three other optimization processes: simulated annealing (SA), QA without problem decomposition, and QA with an existing D-Wave problem decomposition strategy, and conducted an empirical evaluation with three real-world TCM datasets. Results show that BootQA outperforms QA without problem decomposition and QA with the existing decomposition strategy regarding effectiveness. Moreover, BootQA ’s effectiveness is similar to SA. Finally, BootQA has higher efficiency in terms of time when solving large TCM problems than the other three optimization processes.

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Black-box Test Case Selection by Relating Code Changes with Previously Fixed Defects

Cited by 2 publications

References 8 publications

State of Practical Applicability of Regression Testing Research: A Live Systematic Literature Review

State of Practical Applicability of Regression Testing Research: A Live Systematic Literature Review

Test Case Minimization with Quantum Annealers

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