Automated behavioral testing of refactoring engines

Soares, Gustavo

doi:10.1145/2384716.2384737

Cited by 17 publications

(22 citation statements)

References 9 publications

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“…However, it is difficult for tool builders to identify all preconditions needed for each refactoring. Even state-of-the-art academic and industrial Java refactoring tools may introduce bugs [6]. Also, Ge et al [4] show that programmers sometimes fail to recognize that they are going to refactor before manually starting the refactoring, leading to an underuse of refactoring tools.…”

Section: Methodsmentioning

confidence: 99%

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Live feedback on behavioral changes

Soares

Murphy-Hill

Gheyi

2013

2013 1st International Workshop on Live Programming (LIVE)

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Abstract-The costs to find and fix bugs grows over time, to the point where fixing a bug after release may cost as much as 100 times more than before release. To help programmers find bugs as soon as they are introduced, we sketch a plugin for an integrated development environment that provides live feedback about behavioral changes to Java programs by continuously generating tests, running the tests on the current and previous versions of the program, and comparing the results. Such a tool would allow programmers to better understand how their changes affect the behavior of their programs. As a proof of concept, we developed a prototype that found a bug that remained undetected by pair programmers working on JHotDraw in a previous study. Had the programmers performed this change with our plugin, they would have been notified about the bug as soon as they introduced it.

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Section: Methodsmentioning

confidence: 99%

“…We used the random test generator Randoop [5] to generate tests for SafeRefactor, which has helped us to detect a number of behavioral changes in Java programs [3], [6]. Other options include Seeker, a test generator that combines static and dynamic analysis for generating test suites [7].…”

Section: Challengesmentioning

confidence: 99%

Live feedback on behavioral changes

Soares

Murphy-Hill

Gheyi

2013

2013 1st International Workshop on Live Programming (LIVE)

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“…Except for a renaming refactoring tool based on CompCert [8], none of these tools is mechanically certified and unfortunately, none is exempt from bugs as shown by a recent study [20].…”

Section: Related Workmentioning

confidence: 99%

Verifiable semantic difference languages

Girka

Mentré

Régis-Gianas

2017

Proceedings of the 19th International Symposium on Principles and Practice of Declarative Programming

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Program differences are usually represented as textual differences on source code with no regard to its syntax or its semantics. In this paper, we introduce semantic-aware difference languages. A difference denotes a relation between program reduction traces. A difference language for the toy imperative programming language Imp is given as an illustration.To certify software evolutions, we want to mechanically verify that a difference correctly relates two given programs. Product programs and correlating programs are effective proof techniques for relational reasoning. A product program simulates, in the same programming language as the compared programs, a well-chosen interleaving of their executions to highlight a specific relation between their reduction traces. While this approach enables the use of readily-available static analysis tools on the product program, it also has limitations: a product program will crash whenever one of the two programs under consideration crashes, thus making it unsuitable to characterize a patch fixing a safety issue.We replace product programs by correlating oracles which need not be expressed in the same programming language as the compared programs. This allows designing correlating oracle languages specific to certain classes of program changes and capable of relating crashing programs with non-crashing ones. Thanks to oracles, the primitive differences of our difference language on Imp can be assigned a verifiable semantics. Besides, each class of oracles comes with a specific proof scheme which simplifies relational reasoning for a well-specified class of relations. We also prove that our framework is at least as expressive as several Relational Hoare Logic variants by encoding them as correlating oracles, (re)proving soundness of those variants in the process. The entirety of the framework as well as its instantiations have been defined and proved correct using the Coq proof assistant.

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“…For each subject system, we used a substantial test suite to provide evidence for the correctness of our refactoring engine. For this purpose, we extended a standard approach of testing refactoring engines [16], [24], [54] with support for variability.…”

Section: Introductionmentioning

confidence: 99%

“…To this end, we extend a standard approach of testing refactoring engines [16], [24], [54] with support for variability.…”

Section: Introductionmentioning

confidence: 99%

Morpheus: Variability-Aware Refactoring in the Wild

Liebig

Apel

Janker

et al. 2015

2015 IEEE/ACM 37th IEEE International Conference on Software Engineering

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Abstract-Today, many software systems are configurable with conditional compilation. Just like any software system, configurable systems need to be refactored in their evolution, but their inherent variability induces an additional dimension of complexity that is not addressed well by current academic and industrial refactoring engines. To improve the state of the art, we propose a variability-aware refactoring approach that relies on a canonical variability representation and recent work on variability-aware analysis. The goal is to preserve the behavior of all variants of a configurable system, without compromising general applicability and scalability. To demonstrate practicality, we developed MORPHEUS, a sound, variability-aware refactoring engine for C code with preprocessor directives. We applied MORPHEUS to three substantial real-world systems (BUSYBOX, OPENSSL, and SQLITE) showing that it scales reasonably well, despite of its heavy reliance on satisfiability solvers. By extending a standard approach of testing refactoring engines with support for variability, we provide evidence for the correctness of the refactorings implemented.

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Automated behavioral testing of refactoring engines

Cited by 17 publications

References 9 publications

Live feedback on behavioral changes

Live feedback on behavioral changes

Verifiable semantic difference languages

Morpheus: Variability-Aware Refactoring in the Wild

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