Improving  the  Accuracy  of  Static  Source  Code  Based  Software  Change  Impact Analysis Through Hybrid Techniques: A Review

Shakirat, Yusuff; Bajeh, Amos Orenyi; Aro, Taye; Adewole, Kayode S.; Mathematical, Departmentof

doi:10.15282/ijsecs.7.1.2021.6.0082

Cited by 5 publications

(1 citation statement)

References 25 publications

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“…Wiese [29] found that contextual information, such as commit metadata and developer communication, can significantly enhance prediction models, reducing false recommendations. Similarly, Shakirat [30] reviewed hybrid techniques for software change impact analysis, highlighting their potential to enhance accuracy. These studies and others such [31], [32] that leveraged hybrid approaches to change-prone prediction collectively underscore the potential of hybrid approaches in improving the accuracy of software co-change prediction.…”

Section: Related Workmentioning

confidence: 99%

Enhancing Software Co-Change Prediction: Leveraging Hybrid Approaches for Improved Accuracy

Zagane,

Alenezi

2024

IEEE Access

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Accurate prediction of co-changes in software systems is crucial for efficient development and maintenance, especially as systems grow in complexity. While deep learning-based approaches have shown promise, they often struggle with diverse and complex data. In this paper, we present a novel hybrid approach that combines traditional software engineering methods with deep learning techniques to improve co-change prediction accuracy. Our approach leverages software metrics and deep learning models, incorporating the unique characteristics induced by naming conventions, such as PascalCase and camelCase, used by developers for naming consistency. By utilizing char n-gram embedding and sub-token context, we enrich the vector representations of source file names, capturing relationships and dependencies between files. We comprehensively evaluate our hybrid approach using three open-source software projects. The findings of this study have significant implications for the development of more effective software co-change prediction tools and techniques, enabling better decision-making in software development and maintenance processes. Our approach outperforms traditional software engineering methods and deep learning-based approaches, demonstrating its potential to significantly improve software development and maintenance efficiency.

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Section: Related Workmentioning

confidence: 99%

Enhancing Software Co-Change Prediction: Leveraging Hybrid Approaches for Improved Accuracy

Zagane,

Alenezi

2024

IEEE Access

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Regression test prioritization leveraging source code similarity with tree kernels

Altiero,

Corazza,

Di Martino

et al. 2024

J Software Evolu Process

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Regression test prioritization (RTP) is an active research field, aiming at re‐ordering the tests in a test suite to maximize the rate at which faults are detected. A number of RTP strategies have been proposed, leveraging different factors to reorder tests. Some techniques include an analysis of changed source code, to assign higher priority to tests stressing modified parts of the codebase. Still, most of these change‐based solutions focus on simple text‐level comparisons among versions. We believe that measuring source code changes in a more refined way, capable of discriminating between mere textual changes (e.g., renaming of a local variable) and more structural changes (e.g., changes in the control flow), could lead to significant benefits in RTP, under the assumption that major structural changes are also more likely to introduce faults. To this end, we propose two novel RTP techniques that leverage tree kernels (TK), a class of similarity functions largely used in Natural Language Processing on tree‐structured data. In particular, we apply TKs to abstract syntax trees of source code, to more precisely quantify the extent of structural changes in the source code, and prioritize tests accordingly. We assessed the effectiveness of the proposals by conducting an empirical study on five real‐world Java projects, also used in a number of RTP‐related papers. We automatically generated, for each considered pair of software versions (i.e., old version, new version) in the evolution of the involved projects, 100 variations with artificially injected faults, leading to over 5k different software evolution scenarios overall. We compared the proposed prioritization approaches against well‐known prioritization techniques, evaluating both their effectiveness and their execution times. Our findings show that leveraging more refined code change analysis techniques to quantify the extent of changes in source code can lead to relevant improvements in prioritization effectiveness, while typically introducing negligible overheads due to their execution.

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An improving approach to analyzing change impact of C programs

Dai

Wang

Jin

et al. 2022

Computer Communications

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Improving the Accuracy of Static Source Code Based Software Change Impact Analysis Through Hybrid Techniques: A Review

Cited by 5 publications

References 25 publications

Enhancing Software Co-Change Prediction: Leveraging Hybrid Approaches for Improved Accuracy

Enhancing Software Co-Change Prediction: Leveraging Hybrid Approaches for Improved Accuracy

Regression test prioritization leveraging source code similarity with tree kernels

An improving approach to analyzing change impact of C programs

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