Selecting Open Source Projects for Traceability Case Studies

Rath, Michael; Tomova, Mihaela Todorova; Mäder, Patrick

doi:10.1007/978-3-030-15538-4_17

Cited by 3 publications

(3 citation statements)

References 15 publications

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“…What that means is that the trackers used by these projects internally integrate a well-structured design flow where the status of bugs undergoes several checkcycles until they get fixed. Finally, these projects have been extensively used by other researchers for similar domain [207,208,299,302]. In terms of project categorization, the first four projects (i.e., Derby, Drools, Groovy, and Maven) belong to a Jira tracker.…”

Section: Practical Benefitsmentioning

confidence: 99%

“…In our approach, candidate keywords are extracted in a new way derived from a combination of graph-based method (TextRank), statistical-based method (TF-IDF), and feature-based method (Yake). The length of keywords that form the keyword summary was set to 10 as a common retrieval threshold, which is accepted software engineering practices supported by other researchers [134,410] including the requirements engineering domain [207,302]. The optimal number of keywords is an open research question.…”

Section: Keyconcept Formationmentioning

confidence: 99%

“…Our premise is that a short list of 10 keywords suffices to determine the overall content of each source file (i.e., each class). It should be noted that this pruned list of 10 keywords is a common threshold used in software engineering practices [134,207,302,410].…”

Section: Combined Ranking Scoresmentioning

confidence: 99%

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Exploring the Software Quality Maze: Detecting Scattered and Tangled Crosscutting Quality Concerns in Source Code in Support of Maintenance Tasks

Krasniqi

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Software quality attributes, such as reliability, security, and usability, are often well-defined and understood at the requirement level. They lay the ground foundation necessary to achieve high-quality, robust, user-friendly, and trustworthy software systems. However, when addressing these attributes at the code level, two significant challenges emerge. First, they tend to scatter across the codebase due to improper encapsulation of object-oriented classes, hampering the visibility of quality-related components across the codebase. Second, they become tangled within a single module due to intricate interdependencies with functional aspects of the code. Addressing quality concerns in the presence of scattered and tangled code can lead to unforeseen issues. For example, software developers may inadvertently introduce new and latent bugs or incorrectly implement code components deviating from the original system-wide requirements. To tackle these pressing issues, this dissertation proposes a series of state-of-the-art solutions integrating ML-based techniques and NLP-based techniques, including static program analysis techniques, to automatically and effectively detect and repair quality concerns present at the code level, even when scattered across the codebase. Additionally, we introduce program structural analysis and change impact analysis, complemented by other unsupervised ML-based techniques, to disentangle quality-related changes from functional ones, to gain a holistic understanding of a particular maintenance task. From a practical stance, our techniques can be integrated for quality risk assessment purposes and incorporated into safety-critical continuous integration systems to monitor the evolving nature of quality concerns. Furthermore, they can enhance bidirectional traceability across evolving software artifacts, such as bug reports, commit messages, and source code, and facilitate bug triaging systems for prioritizing the resolution of highly impacted quality-related bugs.

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Section: Practical Benefitsmentioning

confidence: 99%