ReAssert

Daniel, Brett; Dig, Danny; Gvero, Tihomir; Jagannath, Vilas; Jiaa, Johnston; Mitchell, Damion; Nogiec, Jurand; Tan, Shin Hwei; Marinov, Darko

doi:10.1145/1985793.1985978

Cited by 13 publications

(1 citation statement)

References 7 publications

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“…For this purpose, we choose Java and Python, two languages considered to be popular by programming language rankings such as PYPL. 11 If rotten green tests do exist in other languages, we then want to find out if our previous categorization [13] is also valid for these languages. In other words, are we able to identify missed fail, missed skip, context-dependent assertions,…”

Section: Research Questionsmentioning

confidence: 99%

Rotten green tests in Java, Pharo and Python

et al. 2021

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Section: Research Questionsmentioning

confidence: 99%

Rotten green tests in Java, Pharo and Python

et al. 2021

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TarTar: A Timed Automata Repair Tool

Kölbl

Leue

Wies

2020

Computer Aided Verification

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We present TarTar , an automatic repair analysis tool that, given a timed diagnostic trace (TDT) obtained during the model checking of a timed automaton model, suggests possible syntactic repairs of the analyzed model. The suggested repairs include modified values for clock bounds in location invariants and transition guards, adding or removing clock resets, etc. The proposed repairs guarantee that the given TDT is no longer feasible in the repaired model, while preserving the overall functional behavior of the system. We give insights into the design and architecture of TarTar , and show that it can successfully repair 69% of the seeded errors in system models taken from a diverse suite of case studies.

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Is This a Bug or an Obsolete Test?

Hao

Lan

Zhang

et al. 2013

ECOOP 2013 – Object-Oriented Programming

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Abstract. In software evolution, developers typically need to identify whether the failure of a test is due to a bug in the source code under test or the obsoleteness of the test code when they execute a test suite. Only after finding the cause of a failure can developers determine whether to fix the bug or repair the obsolete test. Researchers have proposed several techniques to automate test repair. However, test-repair techniques typically assume that test failures are always due to obsolete tests. Thus, such techniques may not be applicable in real world software evolution when developers do not know whether the failure is due to a bug or an obsolete test. To know whether the cause of a test failure lies in the source code under test or in the test code, we view this problem as a classification problem and propose an automatic approach based on machine learning. Specifically, we target Java software using the JUnit testing framework and collect a set of features that may be related to failures of tests. Using this set of features, we adopt the Best-first Decision Tree Learning algorithm to train a classifier with some existing regression test failures as training instances. Then, we use the classifier to classify future failed tests. Furthermore, we evaluated our approach using two Java programs in three scenarios (within the same version, within different versions of a program, and between different programs), and found that our approach can effectively classify the causes of failed tests.

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ReAssert

Cited by 13 publications

References 7 publications

Rotten green tests in Java, Pharo and Python

Rotten green tests in Java, Pharo and Python

TarTar: A Timed Automata Repair Tool

Is This a Bug or an Obsolete Test?

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