Automated training-set creation for software architecture traceability problem

Zogaan, Waleed; Mujhid, Ibrahim; Santos, Joanna C. S.; Gonzalez, Danielle; Mirakhorli, Mehdi

doi:10.1007/s10664-016-9476-y

Cited by 7 publications

(2 citation statements)

References 22 publications

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“…( 2), It measures the relevance of retrieved documents [20]. Recall refers to the correctly identified links [21] percentage as shown in Eq. ( 3).…”

Section: Introductionmentioning

confidence: 99%

An Enhancing Recovery Links between Two Artifacts Using Variational Autoencoder

2024

IJIES

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Traceability link recovery is a crucial task in software engineering that ensures the development of dependable and credible software systems. Traceability links between requirements and source code support various activities in the software development process, including change management and software maintenance. These links can be established manually or automatically. Manual trace retrieval is a time-consuming task. Automatic trace retrieval can be performed via various tools such as information retrieval or machine learning methods. Some automatic tools couldn't retrieve the links between requirements and source code. Meanwhile, a big concern associated with automated trace retrieval is the low precision problem primarily caused by the term mismatches across documents to be traced. This study proposes an approach that addresses the low precision problem caused by the term mismatch problem between requirements and source code to obtain the greatest improvements in trace retrieval accuracy. The proposed approach utilizes a variational autoencoder (VAE), an unsupervised deep-learning model in the automated trace retrieval process. We have conducted a series of experiments on three datasets: eTour, SMOS, and eANCI to evaluate our approach against existing approaches. In order to validate the effectiveness of our proposed approach, we compared it to three previous studies that addressed the same problem and utilized the same datasets: the first study used unsupervised machine learning based on clustering, the second study used active learning, and the third study used a classification machine learning. The results show that our proposed approach improves the trace retrieval precision in the automated trace retrieval process.

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“…( 2), It measures the relevance of retrieved documents [20]. Recall refers to the correctly identified links [21] percentage as shown in Eq. ( 3).…”

Section: Introductionmentioning

confidence: 99%

An Enhancing Recovery Links between Two Artifacts Using Variational Autoencoder

2024

IJIES

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“…These prior studies solely focused on detecting security concerns [328]. The authors did not follow a standard taxonomy suitable for identifying quality concerns at source code level nor at the design level [423].…”

Section: Detection Of Quality Concerns Across Artifactsmentioning

confidence: 99%

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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