Rotten Green Tests

Delplanque, Julien; Ducasse, Sté́phane; Polito, Guillermo; Black, Andrew P.; Etien, Anne

doi:10.1109/icse.2019.00062

Cited by 14 publications

(28 citation statements)

References 30 publications

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“…It also extends the evaluation of Delplanque et al [13] to a much larger corpus of Pharo projects. We describe how rotten green tests are detected in the JUnit 4 and unittest/pytest testing frameworks, and report on an evaluation of around a hundred mature projects in each language.…”

Section: Introductionmentioning

confidence: 53%

“…a categorization of rotten green tests [13] that applies consistently to Pharo, Java and Python projects;…”

Section: The Contributions Of This Work Are As Followsmentioning

confidence: 99%

“…After our previous experiments in Pharo [13], we proposed a classification based on four categories: missed fail, missed skip, context-dependent assertions, and fully rotten tests. We now review the definitions of these categories.…”

Section: Generate Reportmentioning

confidence: 99%

“…Rotten green tests [13] are tests that are intended by their designer to execute some assertions, but do not actually do so. Such tests are insidious because they pass, giving the impression that some useful property is being validated by assertions they contain.…”

Section: Introductionmentioning

confidence: 99%

“…Prior work by Delplanque et al [13] proposed a method to detect rotten green tests in Pharo [5], using a combination of static analysis and dynamic monitoring of method execution at the granularity of individual call sites.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Rotten green tests in Java, Pharo and Python

et al. 2021

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Section: Introductionmentioning

confidence: 53%

“…a categorization of rotten green tests [13] that applies consistently to Pharo, Java and Python projects;…”

Section: The Contributions Of This Work Are As Followsmentioning

confidence: 99%

Section: Generate Reportmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Rotten green tests in Java, Pharo and Python

et al. 2021

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A comprehensive study of code-removal patches in automated program repair

et al. 2022

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Automatic Program Repair (APR) techniques can promisingly help reduce the cost of debugging. Many relevant APR techniques follow the generate-and-validate approach, that is, the faulty program is iteratively modified with different change operators and then validated with a test suite until a plausible patch is generated. In particular, Kali is a generate-and-validate technique developed to investigate the possibility of generating plausible patches by only removing code. Former studies show that indeed Kali successfully addressed several faults. This paper addresses the single and particular case of code-removal patches in automated program repair. We investigate the reasons and the scenarios that make their creation possible, and the relationship with patches implemented by developers. Our study reveals that code-removal patches are often insufficient to fix bugs, and proposes a comprehensive taxonomy of code-removal patches that provides evidence of the problems that may affect test suites, opening new opportunities for researchers in the field of automatic program repair.

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Small-Amp: Test amplification in a dynamically typed language

et al. 2022

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Some test amplification tools extend a manually created test suite with additional test cases to increase the code coverage. The technique is effective, in the sense that it suggests strong and understandable test cases, generally adopted by software engineers. Unfortunately, the current state-of-the-art for test amplification heavily relies on program analysis techniques which benefit a lot from explicit type declarations present in statically typed languages. In dynamically typed languages, such type declarations are not available and as a consequence test amplification has yet to find its way to programming languages like Smalltalk, Python, Ruby and Javascript. We propose to exploit profiling information —readily obtainable by executing the associated test suite— to infer the necessary type information creating special test inputs with corresponding assertions. We evaluated this approach on 52 selected test classes from 13 mature projects in the Pharo ecosystem containing approximately 400 test methods. We show the improvement in killing new mutants and mutation coverage at least in 28 out of 52 test classes (≈ 53%). Moreover, these generated tests are understandable by humans: 8 out of 11 pull-requests submitted were merged into the main code base (≈ 72%). These results are comparable to the state-of-the-art, hence we conclude that test amplification is feasible for dynamically typed languages.

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Rotten Green Tests

Cited by 14 publications

References 30 publications

Rotten green tests in Java, Pharo and Python

Rotten green tests in Java, Pharo and Python

A comprehensive study of code-removal patches in automated program repair

Small-Amp: Test amplification in a dynamically typed language

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