Using source code metrics to predict change-prone Java interfaces

Romano, Daniele; Pinzger, Martin

doi:10.1109/icsm.2011.6080797

Cited by 107 publications

(126 citation statements)

References 33 publications

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“…Recent studies showed that interfaces which do not respect the ISP (as measured by the Service Interface Usage Cohesion (SIUC) metric [14]) are more likely to be changeprone interfaces than those respecting ISP [3]. Yet, we still do not know if those change-prone interfaces do not abide the ISP because they adhere to another design principle, or just because they are badly designed as 'fat' interfaces.…”

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confidence: 83%

“…Interfaces are meant to govern interactions between semi-independent software classes and to encode shared similarities between different class-types [4], [1]. Good design and use of interfaces can significantly ease program comprehension and software maintainability by fostering modularization and minimizing the impact caused by changes in the software implementation [5], [1], [3]. However, designing good interfaces is a sensitive task with a large influence on the rest of the system [5], [2].…”

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confidence: 99%

“…INTRODUCTION Nowadays, interfaces are frequently used as central design elements in object-oriented applications [1], [2], [3]. Interfaces are meant to govern interactions between semi-independent software classes and to encode shared similarities between different class-types [4], [1].…”

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How We Design Interfaces, and How to Assess It

Abdeen

Sahraoui

Shata

2013

2013 IEEE International Conference on Software Maintenance

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Abstract-Interfaces are widely used in Java applications as central design elements for modular programming to increase program reusability and to ease maintainability of software systems. Despite the importance of interfaces and a considerable research effort that has investigated code quality and concrete classes' design, few works have investigated interfaces' design. In this paper, we empirically study interfaces' design and its impact on the design quality of implementing classes (i.e., class cohesion) analyzing twelve Java object-oriented applications. In this study we propose the "Interface-Implementations Model" that we use to adapt class cohesion metrics to assess the cohesion of interfaces based on their implementations. Moreover, we use other metrics that evaluate the conformance of interfaces to the well-known design principles "Program to an Interface, not an implementation" and "Interface Segregation Principle". The results show that software developers abide well by the interface design principles cited above, but they neglect the cohesion property. The results also show that such design practices of interfaces lead to a degraded cohesion of implementing classes, where these latter would be characterized by a worse cohesion than other classes.

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confidence: 83%

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confidence: 99%

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How We Design Interfaces, and How to Assess It

Abdeen

Sahraoui

Shata

2013

2013 IEEE International Conference on Software Maintenance

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“…We find that our approach can achieve a reasonable weighted precision, recall, F-measure, and AUC scores. The AUC of our approach is more than 0.7, and in many past studies, an AUC score above 0.7 is often considered reasonable [20], [27]. Note that we can achieve these reasonable scores by just using 50 labelled examples.…”

Section: Research Questionmentioning

confidence: 97%

Active Semi-supervised Defect Categorization

Thung

2015

2015 IEEE 23rd International Conference on Program Comprehension

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Abstract-Defects are inseparable part of software development and evolution. To better comprehend problems affecting a software system, developers often store historical defects and these defects can be categorized into families. IBM proposes Orthogonal Defect Categorization (ODC) which include various classifications of defects based on a number of orthogonal dimensions (e.g., symptoms and semantics of defects, root causes of defects, etc.). To help developers categorize defects, several approaches that employ machine learning have been proposed in the literature. Unfortunately, these approaches often require developers to manually label a large number of defect examples. In practice, manually labelling a large number of examples is both time-consuming and labor-intensive. Thus, reducing the onerous burden of manual labelling while still being able to achieve good performance is crucial towards the adoption of such approaches. To deal with this challenge, in this work, we propose an active semi-supervised defect prediction approach. It is performed by actively selecting a small subset of diverse and informative defect examples to label (i.e., active learning), and by making use of both labeled and unlabeled defect examples in the prediction model learning process (i.e., semi-supervised learning). Using this principle, our approach is able to learn a good model while minimizing the manual labeling effort.To evaluate the effectiveness of our approach, we make use of a benchmark dataset that contains 500 defects from three software systems that have been manually labelled into several families based on ODC. We investigate our approach's ability in achieving good classification performance, measured in terms of weighted precision, recall, F-measure, and AUC, when only a small number of manually labelled defect examples are available. Our experiment results show that our active semi-supervised defect categorization approach is able to achieve a weighted precision, recall, F-measure, and AUC of 0.651, 0.669, 0.623, and 0.710, respectively, when only 50 defects are manually labelled. Furthermore, it outperforms an existing active multiclass classification algorithm, proposed in the machine learning community, by a substantial margin.

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“…Evolutionary development has been proposed as an efficient way to deal with risks such as new technology and imprecise or changing requirements [15]. Changes are made to add new features, to adapt to a new environment, to fix bugs or to re-factor the source code [4]. When adapting a system to new usage patterns or technologies, it is necessary to foresee what such adaptations of architectural design imply in terms of system quality [1].…”

Section: Introductionmentioning

confidence: 99%

Understanding Change Prone Classes in Object Oriented Software

Godara¹,

Singh²

2014

IJCA

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Classes in Object Oriented Systems are continuously subjected to changes and defect prone. Predicting such classes is a key research area in the field of software engineering. It is important to identify such change prone classes and defect prone classes. Identifying change prone classes can help developers to build quality software on time. Considering all the above issues, this paper covers the following key issues: 1) identification of change prone classes using various approaches 2) How changes in one class affects multiple classes associated with it. 3) Study Dependency between classes and their effects.

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Using source code metrics to predict change-prone Java interfaces

Cited by 107 publications

References 33 publications

How We Design Interfaces, and How to Assess It

How We Design Interfaces, and How to Assess It

Active Semi-supervised Defect Categorization

Understanding Change Prone Classes in Object Oriented Software

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