Practical isolation of failure-inducing changes for debugging regression faults

Yu, Kai; Lin, Mengxiang; Chen, Jin; Zhang, Xiangyu

doi:10.1145/2351676.2351681

Cited by 27 publications

(21 citation statements)

References 24 publications

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“…This paper unifies two widely studied dimensions of software changes -mutation changes [4,9,12,14,18,20,34,42,43,56] and developer edits [5,39,40,45,50,51,57] -and presents our methodology of fault injection for fault localization (FIFL). Our insight is that the essence of failureinducing edits made by the developer can be captured using mechanical program transformations (i.e., mutation changes in this paper).…”

Section: Resultsmentioning

confidence: 99%

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Injecting mechanical faults to localize developer faults for evolving software

2013

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This paper presents a novel methodology for localizing faults in code as it evolves. Our insight is that the essence of failure-inducing edits made by the developer can be captured using mechanical program transformations (e.g., mutation changes). Based on the insight, we present the FIFL framework, which uses both the spectrum information of edits (obtained using the existing FAULTTRACER approach) as well as the potential impacts of edits (simulated by mutation changes) to achieve more accurate fault localization. We evaluate FIFL on real-world repositories of nine Java projects ranging from 5.7KLoC to 88.8KLoC. The experimental results show that FIFL is able to outperform the stateof-the-art FAULTTRACER technique for localizing failureinducing program edits significantly. For example, all 19 FIFL strategies that use both the spectrum information and simulated impact information for each edit outperform the existing FAULTTRACER approach statistically at the significance level of 0.01. In addition, FIFL with its default settings outperforms FAULTTRACER by 2.33% to 86.26% on 16 of the 26 studied version pairs, and is only inferior than FAULTTRACER on one version pair.

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Section: Resultsmentioning

confidence: 99%

“…Delta Debugging [32,51,53,54] is another methodology for localizing failure-inducing edits. Delta Debugging iteratively applies a subset of all changes to construct intermediate versions to find a minimum set of changes that lead to a test failure.…”

Section: Localizing Failure-inducing Editsmentioning

confidence: 99%

Injecting mechanical faults to localize developer faults for evolving software

2013

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“…Other types of debugging technique include slicing [3], focusing on anomalies [3], mining dynamic call graphs [15], statistical debugging [16], spectra-based fault localization [5], angelic debugging [6], data structure repair [8], relative debugging [17], automatic breakpoint generation [18] and automatic program fixing using contracts [7]. Finally, several combinations of the techniques exist [19]- [23]. Orso presents a detailed explanation and evaluation of some of these automated debugging techniques [24].…”

Section: A Related Workmentioning

confidence: 99%

How developers debug

Beller

Spruit

Zaidman

2017

Preprint

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Debugging software is an inevitable chore, often difficult and more time-consuming than expected, giving it the nickname the " dirty little secret of computer science." Surprisingly, we have little knowledge on how software engineers debug software problems in the real world, whether they use dedicated debugging tools, and how knowledgeable they are about debugging. This study aims to shed light on these aspects by following a mixedmethods research approach. We conduct an online survey capturing how 176 developers reflect on debugging. We augment this subjective survey data with objective observations from how 458 developers use the debugger included in their Integrated Development Abstract-Debugging software is an inevitable chore, often difficult and more time-consuming than expected, giving it the nickname the "dirty little secret of computer science." Surprisingly, we have little knowledge on how software engineers debug software problems in the real world, whether they use dedicated debugging tools, and how knowledgeable they are about debugging. This study aims to shed light on these aspects by following a mixed-methods research approach. We conduct an online survey capturing how 176 developers reflect on debugging. We augment this subjective survey data with objective observations from how 458 developers use the debugger included in their Integrated Development Environments (IDEs) by instrumenting the popular ECLIPSE and INTELLIJ IDEs with our purpose-built plugin WATCHDOG 2.0. To better explain the insights and controversies obtained from the previous steps, we followed up by conducting interviews with debugging experts and regular debugging users. Our results indicate that the the IDE-provided debugger is not used as often as expected, since "printf debugging" remains a feasible choice for many programmers. Furthermore, both knowledge and use of advanced debugging features are low. Our results call to strengthen hands-on debugging experience in Computer Science curricula and can and have already influenced the design of modern IDE debuggers.

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“…For example, [17] presents a combination of coverage analysis and debugging to automatically isolate failure-inducing changes. Similarly, the authors of [10] developed a technique to reduce the number of false memory leak warnings generated by static analysis techniques by automatically validating those warnings.…”

Section: Background and Related Workmentioning

confidence: 99%

Towards an automated approach to use expert systems in the performance testing of distributed systems

Portillo-Dominguez

Wang

Murphy

et al. 2014

Proceedings of the 2014 Workshop on Joining AcadeMiA and Industry Contributions to Test Automation and Model-Based Testing

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Performance testing in distributed environments is challenging. Specifically, the identification of performance issues and their root causes are time-consuming and complex tasks which heavily rely on expertise. To simplify these tasks, many researchers have been developing tools with built-in expertise. However limitations exist in these tools, such as managing huge volumes of distributed data, that prevent their efficient usage for performance testing of highly distributed environments. To address these limitations, this paper presents an adaptive framework to automate the usage of expert systems in performance testing. Our validation assessed the accuracy of the framework and the time savings that it brings to testers. The results proved the benefits of the framework by achieving a significant decrease in the time invested in performance analysis and testing.

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Practical isolation of failure-inducing changes for debugging regression faults

Cited by 27 publications

References 24 publications

Injecting mechanical faults to localize developer faults for evolving software

Injecting mechanical faults to localize developer faults for evolving software

How developers debug

Towards an automated approach to use expert systems in the performance testing of distributed systems

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