Recovering from a decade: a systematic mapping of information retrieval approaches to software traceability

Borg, Markus; Runeson, Per; Ardö, Anders

doi:10.1007/s10664-013-9255-y

Cited by 127 publications

(128 citation statements)

References 149 publications

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“…A majority of the effort has been spent in recovering the traceability links [30]. Our work advances the literature by showing that traceability can be more than recovered.…”

Section: Related Workmentioning

confidence: 69%

“…In most cases, a recall of 90% is achievable at precision levels of 5-30% [24,27,28,29]. Before choosing an accurate tracing mechanism among the many options [14,30] or even developing a new one, we must understand how traceability is currently practiced in justin-time RE. The pertinent literature, however, is surprisingly scant.…”

Section: A Using Traceability To Locate "Where"mentioning

confidence: 99%

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Traceability-enabled refactoring for managing just-in-time requirements

Niu

Bhowmik

Liu

et al. 2014

2014 IEEE 22nd International Requirements Engineering Conference (RE)

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Abstract-Just-in-time requirements management, characterized by lightweight representation and continuous refinement of requirements, fits many iterative and incremental development projects. Being lightweight and flexible, however, can cause wasteful and procrastinated implementation, leaving certain stakeholder goals not satisfied. This paper proposes traceabilityenabled refactoring aimed at fulfilling more requirements fully. We make a novel use of requirements traceability to accurately locate where the software should be refactored, and develop a new scheme to precisely determine what refactorings should be applied to the identified places. Our approach is evaluated through an industrial study. The results show that our approach recommends refactorings more appropriately than a contemporary recommender.

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“…A majority of the effort has been spent in recovering the traceability links [30]. Our work advances the literature by showing that traceability can be more than recovered.…”

Section: Related Workmentioning

confidence: 69%

Section: A Using Traceability To Locate "Where"mentioning

confidence: 99%

Traceability-enabled refactoring for managing just-in-time requirements

Niu

Bhowmik

Liu

et al. 2014

2014 IEEE 22nd International Requirements Engineering Conference (RE)

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“…There are two main families of IR models used in software engineering: algebraic models and probabilistic models (Borg et al, 2014). For automated bug assignment, four studies used algebraic models (Chen et al, 2011;Kagdi et al, 2012;Nagwani and Verma, 2012;Shokripour et al, 2012).…”

Section: Other Approaches To Automated Bug Assignmentmentioning

confidence: 99%

“…For the textual features, we limit the number of words because of memory applications (Borg et al, 2014 Company Telecom contain information in more than 100 elds. However, most of these elds are empty when the bug report is submitted.…”

Section: Bug Report Feature Selectionmentioning

confidence: 99%

Automated bug assignment: Ensemble-based machine learning in large scale industrial contexts

et al. 2015

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Context: Bug report assignment is an important part of software maintenance. In particular, incorrect assignments of bug reports to development teams can be very expensive in large software development projects.Several studies propose automating bug assignment techniques using machine learning in open source software contexts, but no study exists for large-scale proprietary projects in industry. Objective: The goal of this study is to evaluate automated bug assignment techniques that are based on machine learning classication. In particular, we study the state-of-the-art ensemble learner Stacked Generalization (SG) that combines several classiers. Method: We collect more than 50,000 bug reports from ve development projects from two companies in dierent domains. We implement automated bug assignment and evaluate the performance in a set of controlled experiments. Results: We show that SG scales to large scale industrial application and that it outperforms the use of individual classiers for bug assignment, reaching prediction accuracies from 50% to 90% when large training sets are used. In addition, we show how old training data can decrease the prediction accuracy of bug assignment. Conclusions: We advice industry to use SG for bug assignment in proprietary contexts, using at least 2,000 bug reports for training. Finally, we highlight the importance of not solely relying on results from cross-validation when evaluating automated bug assignment.

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“…The literature also reports on the extension and adaptation of commercial tools for impact analysis purposes [67]. Regarding the current approaches for automated traceability and impact analysis, they have been validated on small data sets [7] and limited artefact types [49]. Therefore, their actual support to industrial needs remains unconfirmed because practitioners usually have to deal with tens of artefact types and thousands of artefacts for SECIA [48].…”

Section: General Literature On Impact Analysismentioning

confidence: 99%

An Industrial Survey of Safety Evidence Change Impact Analysis Practice

Vara

Borg

Wnuk

et al. 2016

IIEEE Trans. Software Eng.

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Abstract-Context. In many application domains, critical systems must comply with safety standards. This involves gathering safety evidence in the form of artefacts such as safety analyses, system specifications, and testing results. These artefacts can evolve during a system's lifecycle, creating a need for change impact analysis to guarantee that system safety and compliance are not jeopardised. Objective. We aim to provide new insights into how safety evidence change impact analysis is addressed in practice. The knowledge about this activity is limited despite the extensive research that has been conducted on change impact analysis and on safety evidence management. Method. We conducted an industrial survey on the circumstances under which safety evidence change impact analysis is addressed, the tool support used, and the challenges faced. Results. We obtained 97 valid responses representing 16 application domains, 28 countries, and 47 safety standards. The respondents had most often performed safety evidence change impact analysis during system development, from system specifications, and fully manually. No commercial change impact analysis tool was reported as used for all artefact types and insufficient tool support was the most frequent challenge. Conclusion. The results suggest that the different artefact types used as safety evidence co-evolve. In addition, the evolution of safety cases should probably be better managed, the level of automation in safety evidence change impact analysis is low, and the state of the practice can benefit from over 20 improvement areas.

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Recovering from a decade: a systematic mapping of information retrieval approaches to software traceability

Cited by 127 publications

References 149 publications

Traceability-enabled refactoring for managing just-in-time requirements

Traceability-enabled refactoring for managing just-in-time requirements

Automated bug assignment: Ensemble-based machine learning in large scale industrial contexts

An Industrial Survey of Safety Evidence Change Impact Analysis Practice

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