The effect of hoisting on variants of Hierarchical Delta Debugging. Issue 11 (19th June 2022)

• Record Type: Journal Article
• Authors: Vince, Dániel; Hodován, Renáta; Bársony, Daniella; Kiss, Ákos
• Other Names: Miranda Breno guestEditor.; Tuya Javier guestEditor.; Garrido Alejandra guestEditor.
• Is Part Of: Journal of software. Volume 34:Issue 11(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-06-19
• Subjects: Hierarchical Delta Debugging -- hoisting -- test case minimization -- transformation‐based minimization; Software engineering -- Periodicals; Computer software -- Development -- Periodicals; Software maintenance -- Periodicals; 005.1
• DOI: 10.1002/smr.2483
• Languages: English
• ISSNs: 2047-7473

Abstract

Abstract: Minimizing failing test cases is an important preprocessing step on the path of debugging. If much of a test case that triggered a bug does not contribute to the actual failure, then the time required to fix the bug can increase considerably. However, test case reduction itself can be a time‐consuming task, especially if done manually. Therefore, automated minimization techniques have been proposed, the minimizing Delta Debugging and the Hierarchical Delta Debugging (HDD) algorithms being the most well known. In this paper, we investigated the input format of HDD, searching for structures that the algorithm cannot reduce. Motivated by the findings, we have created an algorithmic framework that enabled the use of transformations other than pruning. Furthermore, with the Transformation‐based Minimization framework, we propose to extend HDD and its coarse and recursive variants with a reduction method that does not prune subtrees but replaces them with compatible subtrees further down the hierarchy, called hoisting. We have evaluated various combinations of pruning and hoisting on multiple test suites and found that hoisting can help to further reduce the size of test cases by 27% on average and by 80% as best case compared with the baseline algorithm. Abstract : We investigated the input format of HDD, searching for structures that the algorithm cannot reduce. Motivated by the findings, we have created an algorithmic framework that enabled the use of transformations other than pruning. Furthermore, with the Transformation‐based Minimization framework, we propose to extend HDD and its coarse and recursive variants with a reduction method that does not prune subtrees, but replaces them with compatible subtrees further down the hierarchy, called hoisting.