: Priority Aware Test Case Reduction

Gharachorlu, Golnaz; Sumner, William N.

doi:10.1007/978-3-030-16722-6_24

Cited by 12 publications

(14 citation statements)

References 16 publications

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“…Note that for the reduction of the test cases in this test suite, we have used two additional non-HDD-based state-of-the-art test case reduction tools to give a more comprehensive evaluation of hoisting. The two tools are Perses 5 (revision 34d4dc4, a Java and Kotlin-based implementation of the algorithm of Sun et al [11]) and Pardis 9 (revision b656c6f, by Gharachorlu and Sumner [12]). For details on the algorithms and their implementations, the reader is referred to the corresponding papers and tool documentations.…”

Section: Resultsmentioning

confidence: 99%

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Extending Hierarchical Delta Debugging with Hoisting

Vince¹,

Hodován²,

Bársony³

et al. 2021

Preprint

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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 (DDMIN) and the Hierarchical Delta Debugging (HDD) algorithms being the most well known. DDMIN does not need any information about the structure of the test case, thus it works for any kind of input. If the structure is known, however, it can be utilized to create smaller test cases faster. This is exemplified by HDD, which works on tree-structured inputs, pruning subtrees at each level of the tree with the help of DDMIN.In this paper, we propose to extend HDD 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 as much as 80% compared to the baseline HDD. We have also compared our results to other stateof-the-art test case reduction algorithms and found that HDD extended with hoisting can produce smaller output in most of the cases.Index Terms-test case minimization, hierarchical delta debugging, hoisting * '(' ')' compoundStatement '{' selectionStatement 'if' '(' primaryExpression Constant '1' ')' statement compoundStatement '{' * expressionStatement postfixExpression Identifier 'printf' * '(' StringLiteral '"Hello world!\n"' ')' ';' '}' '}'

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Section: Resultsmentioning

confidence: 99%

“…Built upon the ideas introduced in Perses, Gharachorlu and Sumner [12] extended it in a new framework, named Pardis, with an improved queue prioritization algorithm. This algorithm only considered nullable, i.e., completely removable tree nodes and assigned weights based on a node own token weight instead of its parents.…”

Section: Related Workmentioning

confidence: 99%

Extending Hierarchical Delta Debugging with Hoisting

Vince¹,

Hodován²,

Bársony³

et al. 2021

Preprint

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show abstract

“…When reducing, they apply ddmin to the quantified nodes and hoisting to the non-quantified ones. Pardis [20], from Gharachorlu et al, is built upon the idea of Perses, but it uses a different approach to prioritize the ordering of the worklist.…”

Section: Related Workmentioning

confidence: 99%

Generalizing the Split Factor of the Minimizing Delta Debugging Algorithm

Kiss

2020

IEEE Access

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“…At the same time, it performs on average two times faster than C-Reduce. Gharachorlu et al [14] present a new technique called Pardis, which improves Perses by prioritizing reduction of larger subtrees first. It is shown to work 1.3x to 7.8x faster than Perses, with less reduction soundness checks and same overall reduction quality.…”

Section: Related Workmentioning

confidence: 99%

ReduKtor: How We Stopped Worrying About Bugs in Kotlin Compiler

Stepanov

Akhin

Belyaev

2019

2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE)

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Bug localization is well-known to be a difficult problem in software engineering, and specifically in compiler development, where it is beneficial to reduce the input program to a minimal reproducing example; this technique is more commonly known as delta debugging. What additionally contributes to the problem is that every new programming language has its own unique quirks and foibles, making it near impossible to reuse existing tools and approaches with full efficiency. In this experience paper we tackle the delta debugging problem w.r.t. Kotlin, a relatively new programming language from JetBrains. Our approach is based on a novel combination of program slicing, hierarchical delta debugging and Kotlin-specific transformations, which are synergistic to each other. We implemented it in a prototype called ReduKtor and did extensive evaluation on both synthetic and real Kotlin programs; we also compared its performance with classic delta debugging techniques. The evaluation results support the practical usability of our approach to Kotlin delta debugging and also shows the importance of using both language-agnostic and language-specific techniques to achieve best reduction efficiency and performance.

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: Priority Aware Test Case Reduction

Cited by 12 publications

References 16 publications

Extending Hierarchical Delta Debugging with Hoisting

Extending Hierarchical Delta Debugging with Hoisting

Generalizing the Split Factor of the Minimizing Delta Debugging Algorithm

ReduKtor: How We Stopped Worrying About Bugs in Kotlin Compiler

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