A model for estimating change propagation in software

Ferreira, Kecia A. M.; Bigonha, Mariza A. S.; Bigonha, Roberto da Silva; Lima, Bernardo N. de; Gomes, Bárbara M.; Mendes, Luiz Felipe

doi:10.1007/s11219-017-9358-6

Cited by 3 publications

(2 citation statements)

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“…Some of the trends in recent articles to predict change propagation have been to apply a probabilistic approach and other methods at a different level of abstraction. Ferreira et al [11] introduced K 3 B, a probabilistic model to estimate the impact of change propagation, which can be used to estimate the effort required for software maintenance tasks. Siavash et al [12] used the Bayesian belief network as a probabilistic tool to predict possible models of the affected system, given a change in the system at the package level.…”

Section: Leveraging Changelog Data In Change Propagationmentioning

confidence: 99%

“…For example, a heuristic approach [9] was used to predict a change in one source code element propagating to other elements. Zimmermann et al [10] developed a stochastic model known as K3B, which predicts how far a set of changes propagates throughout the system [11]. Siavash et al [12] used the Bayesian belief network as a probabilistic tool to predict possible models of the affected system, given a change in the system at the package level.…”

Section: Introductionmentioning

confidence: 99%

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FCP2Vec: Deep Learning-Based Approach to Software Change Prediction by Learning Co-Changing Patterns from Changelogs

Ahmed

Lee

2023

Applied Sciences

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As software systems evolve, they become more complex and larger, creating challenges in predicting change propagation while maintaining system stability and functionality. Existing studies have explored extracting co-change patterns from changelog data using data-driven methods such as dependency networks; however, these approaches suffer from scalability issues and limited focus on high-level abstraction (package level). This article addresses these research gaps by proposing a file-level change propagation to vector (FCP2Vec) approach. FCP2Vec is a recommendation system designed to aid developers by suggesting files that may undergo change propagation subsequently, based on the file being presently worked on. We carried out a case study utilizing three publicly available datasets: Vuze, Spring Framework, and Elasticsearch. These datasets, which consist of open-source Java-based software development changelogs, were extracted from version control systems. Our technique learns the historical development sequence of transactional software changelog data using a skip-gram method with negative sampling and unsupervised nearest neighbors. We validate our approach by analyzing historical data from the software development changelog for more than ten years. Using multiple metrics, such as the normalized discounted cumulative gain at K (NDCG@K) and the hit ratio at K (HR@K), we achieved an average HR@K of 0.34 at the file level and an average HR@K of 0.49 at the package level across the three datasets. These results confirm the effectiveness of the FCP2Vec method in predicting the next change propagation from historical changelog data, addressing the identified research gap, and show a 21% better accuracy than in the previous study at the package level.

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Section: Leveraging Changelog Data In Change Propagationmentioning

confidence: 99%