Evaluation of the fixed‐point iteration of minimizing delta debugging

The minimizing Delta Debugging (DDMIN) was among the first algorithms designed to automate the task of reducing test cases. Its popularity is based on the characteristics that it works on any kind of input, without knowledge about the input structure. Several studies proved that smaller outputs can...

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Bibliographic Details
Published in:Journal of software : evolution and process Vol. 36; no. 10
Main Authors: Vince, Dániel, Kiss, Ákos
Format: Journal Article
Language:English
Published: Chichester Wiley Subscription Services, Inc 01.10.2024
Subjects:
Algorithms
Data structures
Debugging
Delta Debugging
evaluation
Software engineering
Software testing
test case minimization
ISSN:2047-7473, 2047-7481
Online Access:Get full text
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Summary:The minimizing Delta Debugging (DDMIN) was among the first algorithms designed to automate the task of reducing test cases. Its popularity is based on the characteristics that it works on any kind of input, without knowledge about the input structure. Several studies proved that smaller outputs can be produced faster with more advanced techniques (e.g., building a tree representation of the input and reducing that data structure); however, if the structure is unknown or changing frequently, maintaining the descriptors might not be resource‐efficient. Therefore, in this paper, we focus on the evaluation of the novel fixed‐point iteration of minimizing Delta Debugging (DDMIN*) on publicly available test suites related to software engineering. Our experiments show that DDMIN* can help reduce inputs further by 48.08% on average compared to DDMIN (using lines as the units of the reduction). Although the effectiveness of the algorithm improved, it comes with the cost of additional testing steps. This study shows how the characteristics of the input affect the results and when it pays off using DDMIN*. This paper focuses on the evaluation of the fixed‐point iteration of the minimizing Delta Debugging algorithm (DDMIN*). Our experiments show that fixed‐point iteration can help reduce inputs further by 48.08% on average (conducted on publicly available test suites using lines as the units of the reduction). This study also shows how the characteristics of the input affect the results and when it pays off using DDMIN*.
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ISSN:2047-7473
2047-7481
DOI:10.1002/smr.2702