Predicting Change Impact in Object-Oriented Applications with Bayesian Networks

Abdi, Mustapha Kamel; Lounis, Hakim; Sahraoui, Houari

doi:10.1109/compsac.2009.38

Cited by 14 publications

(4 citation statements)

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“…Researchers in [27] have investigated the correlation of the cohesion metrics with the impact of write interactions between class members and have proposed revisions to the existing cohesion metrics to consider the impact of the write interactions. Code metrics were also used as features with machine learning techniques to build prediction models of SCIA, Abdi et al in [28] proposed prediction models built using OO coupling metrics and several classification algorithms.…”

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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“…They have used BN to quantify the relationships and elements of the design. On the other hand, Abdi et al [18] proposed a change impact analysis technique that estimates the overall impact of an object-oriented system by using BNs. They used BN by using coupling metrics that refer to two other metrics Design and Implementation.…”

Section: Related Workmentioning

confidence: 99%

Code Change Sniffer: Predicting Future Code Changes with Markov Chain

Ufuktepe

Tuğlular

2021

2021 IEEE 45th Annual Computers, Software, and Applications Conference (COMPSAC)

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Code changes are one of the essential processes of software evolution. These changes are performed to fix bugs, improve quality of software, and provide a better user experience. However, such changes made in code could lead to ripple effects that can cause unwanted behavior. To prevent such issues occurring after code changes, code change prediction, change impact analysis techniques are used. The proposed approach uses static call information, forward slicing, and method change information to build a Markov chain, which provides a prediction for code changes in the near future commits. For static call information, we utilized and compared call graph and effect graph. We performed an evaluation on five open-source projects from GitHub that varies between 5K-26K lines of code. To measure the effectiveness of our proposed approach, recall, precision, and f-measure metrics have been used on five opensource projects. The results show that the Markov chain that is based on call graph can have higher precision compared to effect graph. On the other hand, for small number of cases higher recall values are obtained with effect graph compared to call graph. With a Markov chain model based on call graph and effect graph, we can achieve recall values between 98%-100%.

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“…Hence, some researchers proposed static CIA techniques, which are often performed by analyzing the syntax and semantic, or evolutionary dependence of the program (or its change history repositories) (Abdi et al, 2009a;2009b;Hassaine et al, 2011;Petrenko and Rajlich, 2009;Sun et al, 2010;. According to our previous survey, 70% of the CIA techniques belong to static ones (Li et al, 2012).…”

Section: Introductionmentioning

confidence: 98%