Studying the Advancement in Debugging Practice of Professional Software Developers

Siegmund, Benjamin; Perscheid, Michael; Taeumel, Marcel; Hirschfeld, Robert

doi:10.1109/issrew.2014.36

Cited by 11 publications

(13 citation statements)

References 29 publications

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“…The results in Figure 2 show that over 80% of the developers seem to know all major debugging features found in modern IDEs, strengthening Siegmund's findings [4]. The more advanced features, like defining watches or a suspend policy, seem to be known and used less.…”

Section: B Resultsmentioning

confidence: 55%

“…To the best of our knowledge, only a handful of research efforts exist that empirically evaluated how developers debug. Siegmund et al studied the debugging practices of professional software developers [4], the research effort that is perhaps closest to ours, but different in study population and methodology. They followed eight software developers across four different companies through think-aloud protocols and short interviews.…”

Section: A Related Workmentioning

confidence: 99%

“…4 • A triangulated, large-scale empirical study of how developers debug in reality using a mixed methods approach.…”

Section: Future Work and Conclusionmentioning

confidence: 99%

“…Not only is it inevitable, but Zeller also states that it is difficult and, therefore, consumes much time, often more than creating the bogus piece of software in the first place [3]. During debugging, software engineers need to relate an observed failure to its underlying defect [4]. To complete this step efficiently, they need to have a deep understanding and build a mental model of the software system at hand.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: B Resultsmentioning

confidence: 55%

Section: A Related Workmentioning

confidence: 99%

“…4 • A triangulated, large-scale empirical study of how developers debug in reality using a mixed methods approach.…”

Section: Future Work and Conclusionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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How developers debug

Beller

Spruit

Zaidman

2017

Preprint

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“…What are the major challenging software defects in COBOL and non-COBOL environments? Little is known about developers' point of view on the defects that are most 1 https://www.techrepublic.com/article/everyone-wants-to-retiremainframes-but-74-of-modernization-efforts-fail/ arXiv:2105.01830v1 [cs.SE] 5 May 2021 challenging to locate and fix [22], [23]. The goal of RQ2 is to contrast the types and properties of typical and challenging software defects.…”

Section: Introductionmentioning

confidence: 99%

Contemporary COBOL: Developers' perspectives on Defects and Defect Location

Ciborowska¹,

Chakarov²,

Pandita³

2021

Preprint

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Mainframe systems are facing a critical shortage of developer workforce as the current generation of COBOL developers retires. Furthermore, due to the limited availability of public COBOL resources, entry-level developers, who assume the mantle of legacy COBOL systems maintainers, face significant difficulties during routine maintenance tasks, such as code comprehension and defect location. While we made substantial advances in the field of software maintenance for modern programming languages yearly, mainframe maintenance has received limited attention. With this study, we aim to direct the attention of researchers and practitioners towards investigating and addressing challenges associated with mainframe development. Specifically, we explore the scope of defects affecting COBOL systems and defect location strategies commonly followed by COBOL developers and compare them with the modern programming language counterparts. To this end, we surveyed 30 COBOL and 74 modern Programming Language (PL) developers to understand the differences in defects and defect location strategies employed by the two groups. Our preliminary results show that (1) major defect categories affecting the COBOL ecosystem are different than defects encountered in modern PL software projects; (2) the most challenging defect types in COBOL are also the ones that occur most frequently; and, (3) COBOL and modern PL developers follow similar strategies to locate defective code.

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Explainable automated debugging via large language model-driven scientific debugging

Kang,

Chen,

Yoo

et al. 2024

Empir Software Eng

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Automated debugging techniques have the potential to reduce developer effort in debugging. However, while developers want rationales for the provided automatic debugging results, existing techniques are ill-suited to provide them, as their deduction process differs significantly froof human developers. Inspired by the way developers interact with code when debugging, we propose Automated Scientific Debugging (AutoSD), a technique that prompts large language models to automatically generate hypotheses, uses debuggers to interact with buggy code, and thus automatically reach conclusions prior to patch generation. In doing so, we aim to produce explanations of how a specific patch has been generated, with the hope that these explanations will lead to enhanced developer decision-making. Our empirical analysis on three program repair benchmarks shows that AutoSDperforms competitively with other program repair baselines, and that it can indicate when it is confident in its results. Furthermore, we perform a human study with 20 participants to evaluate AutoSD-generated explanations. Participants with access to explanations judged patch correctness more accurately in five out of six real-world bugs studied. Furthermore, 70% of participants answered that they wanted explanations when using repair tools, and 55% answered that they were satisfied with the Scientific Debugging presentation.

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Studying the Advancement in Debugging Practice of Professional Software Developers

Cited by 11 publications

References 29 publications

How developers debug

How developers debug

Contemporary COBOL: Developers' perspectives on Defects and Defect Location

Explainable automated debugging via large language model-driven scientific debugging

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