Analyzing Change Impact in Object-Oriented Systems

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

doi:10.1109/euromicro.2006.20

Cited by 23 publications

(21 citation statements)

References 8 publications

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“…Lindvall [25] found that larger classes are statistically more change-prone than classes having a small size, and that developers tend to apply more changes to such classes during maintenance and evolution [26]. Further studies showed that coupling metrics are relevant measures to estimate the changeability of source code [27], [28], [29], while Chaumun et al [30] and Tsantalis et al [23] generalized the usefulness of CK metrics [16] for change prediction. The statistical analyses conducted by Lu et al [31] and Malhotra et al [32] clarified which Object Oriented metrics are better suited for change prediction, reporting a set of cohesion, coupling, and inheritance metrics that should be used in this context.…”

Section: Related Workmentioning

confidence: 99%

Developer-Related Factors in Change Prediction: An Empirical Assessment

Catolino

Palomba

Lucia

et al. 2017

2017 IEEE/ACM 25th International Conference on Program Comprehension (ICPC)

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Abstract-Predicting the areas of the source code having a higher likelihood to change in the future is a crucial activity to allow developers to plan preventive maintenance operations such as refactoring or peer-code reviews. In the past the research community was active in devising change prediction models based on structural metrics extracted from the source code. More recently, Elish et al. showed how evolution metrics can be more efficient for predicting change-prone classes. In this paper, we aim at making a further step ahead by investigating the role of different developer-related factors, which are able to capture the complexity of the development process under different perspectives, in the context of change prediction. We also compared such models with existing change-prediction models based on evolution and code metrics. Our findings reveal the capabilities of developer-based metrics in identifying classes of a software system more likely to be changed in the future. Moreover, we observed interesting complementarities among the experimented prediction models, that may possibly lead to the definition of new combined models exploiting developer-related factors as well as product and evolution metrics.

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Section: Related Workmentioning

confidence: 99%

Developer-Related Factors in Change Prediction: An Empirical Assessment

Catolino

Palomba

Lucia

et al. 2017

2017 IEEE/ACM 25th International Conference on Program Comprehension (ICPC)

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“…Abdi et al [78], [79] use static analysis to uncover potentially affected classes and apply machine learning to score each detected, possible impact with probabilities. They consider 13 change types for classes, methods, variables, and distinguish between four types of links between entities (association, aggregation, inheritance, invocation).…”

Section: ) Execution Tracesmentioning

confidence: 99%

A taxonomy for software change impact analysis

Lehnert

2011

Proceedings of the 12th International Workshop on Principles of Software Evolution and the 7th Annual ERCIM Workshop on Softwar

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Abstract-Change impact analysis is required for constantly evolving systems to support the comprehension, implementation, and evaluation of changes. A lot of research effort has been spent on this subject over the last twenty years, and many approaches were published likewise. However, there has not been an extensive attempt made to summarize and review published approaches as a base for further research in the area. Therefore, we present the results of a comprehensive investigation of software change impact analysis, which is based on a literature review and a taxonomy for impact analysis. The contribution of this review is threefold. First, approaches proposed for impact analysis are explained regarding their motivation and methodology. They are further classified according to the criteria of the taxonomy to enable the comparison and evaluation of approaches proposed in literature. We perform an evaluation of our taxonomy regarding the coverage of its classification criteria in studied literature, which is the second contribution. Last, we address and discuss yet unsolved problems, research areas, and challenges of impact analysis, which were discovered by our review to illustrate possible directions for further research.

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“…54. In the fourth scenario the results in relationship proposed between these metrics and change impact contradicted to earlier results [10], leading them to search a hypothesis explaining factors like complexity and system size. This work suggests methods for improving maintenance of software in object oriented systems and focuses on change impact analysis in generic SDLC phases [11].…”

Section: Existing Literature On Software Changeabilitymentioning

confidence: 56%

“…In 2009, M. K. Abdi et al [10] identified the design properties inheritance, coupling, polymorphism that influence software changeability in object oriented design and development. They also formulated a set of rules in object oriented design and development to increase the software quality by increasing their changeability.…”

Section: Object Oriented Designmentioning

confidence: 99%

Changeability Evaluation Model for Object Oriented Software

Goyal¹,

Srivastava²

2017

IJCSIT

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Changeability has a direct relation to software maintainability and has a major role in providing high quality maintainable and trustworthy software. In this paper a correlation between the major attributes of object oriented design and changeability has been ascertained. A changeability evaluation model using multiple linear regression has been proposed for object oriented design. The validation of the proposed changeability evaluation model is made known by means of experimental tests and the results show that the model is highly significant.

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Analyzing Change Impact in Object-Oriented Systems

Cited by 23 publications

References 8 publications

Developer-Related Factors in Change Prediction: An Empirical Assessment

Developer-Related Factors in Change Prediction: An Empirical Assessment

A taxonomy for software change impact analysis

Changeability Evaluation Model for Object Oriented Software

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