A suite of metrics for quantifying historical changes to predict future change‐prone classes in object‐oriented software

Elish, Mahmoud O.; Al-Khiaty, Mojeeb Al-Rhman

doi:10.1002/smr.1549

Cited by 43 publications

(91 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…and DIT ( 3.5 for Findbugs, 2.4 for PMD and 2.3 for Checkstyle.). The results are similar to studies in related fields [11][12][13]. …”

Section: Descriptive Statisticssupporting

confidence: 91%

“…Recent studies have emphasized on change proneness as a relevant software quality attribute [12][13][14][15][16]. Han et al [12] evaluated the change using UML 2.0 models.…”

Section: Related Workmentioning

confidence: 99%

“…They used Behavioral Dependency Measurement approach on JFreeChart, a multi-version open source project. Elish et al [13] investigated the relationship between change proneness and evolution based metrics. These metrics were derived and analyzed using GQM approach.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Applicability of Inter Project Validation for Determination of Change Prone Classes

Malhotra¹,

Gupta²,

Khanna³

2014

IJCA

View full text Add to dashboard Cite

The research in the field of defect and change proneness prediction of software has gained a lot of momentum over the past few years. Indeed, effective prediction models can help software practitioners in detecting the change prone modules of a software, allowing them to optimize the resources used for software testing. However, the development of the prediction models used to determine change prone classes are dependent on the availability of historical data from the concerned software. This can pose a challenge in the development of effective change prediction models. The aim of this paper is to address this limitation by using the data from models based on similar projects to predict the change prone classes of the concerned software. This inter project technique can facilitate the development of generalized models which can be used to ascertain change prone classes for multiple software projects. It would also lead to optimization of critical time and resources in the testing and maintenance phases. This work evaluates the effectiveness of statistical and machine learning techniques for developing such models using receiver operating characteristic analysis. The observations of the study indicate varied results for the different techniques used.

show abstract

“…and DIT ( 3.5 for Findbugs, 2.4 for PMD and 2.3 for Checkstyle.). The results are similar to studies in related fields [11][12][13]. …”

Section: Descriptive Statisticssupporting

confidence: 91%

“…Recent studies have emphasized on change proneness as a relevant software quality attribute [12][13][14][15][16]. Han et al [12] evaluated the change using UML 2.0 models.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Applicability of Inter Project Validation for Determination of Change Prone Classes

Malhotra¹,

Gupta²,

Khanna³

2014

IJCA

View full text Add to dashboard Cite

show abstract

“…More recently, Elish et al [17] started investigating the role of process metrics as predictors of change-prone classes. To this aim, they theoretically and empirically evaluated a new set of metrics (called "evolution metrics") that characterized the history of a class in order to delineate its future changeproneness.…”

Section: Introductionmentioning

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)

View full text Add to dashboard Cite

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.

show abstract

“…[6], code smells [7], design patterns and [8] evolution metrics [9,10]. In terms of the techniques, different machine learning approaches have been used, such as Bayesian networks [11], neural networks [12], multivariate regression [1] and ensemble methods [5].…”

Section: Introductionmentioning

confidence: 99%

Self-learning Change-prone Class Prediction

Yan

Yang

Liu

et al. 2016

International Conferences on Software Engineering and Knowledge Engineering

View full text Add to dashboard Cite

Abstract-Software change-prone class prediction can enhance software decision making activities during software maintenance (e.g., resource allocating). Many change-prone class prediction approaches have been proposed and most are effective in interversion prediction within a project. These approaches usually build a supervised prediction model by learning from historical labeled dataset. However, a major challenge which remains is that this typical change-prone prediction setting cannot be used for new projects or projects with limited historical data. To address this challenge, we propose to tackle this task by adopting a novel prediction method which has not been used in changeprone prediction, namely self-learning method. The key idea of the self-learning method is to enable the change-prone prediction on new projects or projects with limited historical dataset by learning from itself. In this paper, we apply a state-of-art selflearning method, CLAMI, to change-prone prediction. In addition, we propose a novel self-learning approach CLAMI+ by extending CLAMI. The experiments among 14 open source projects show that the self-learning methods achieve comparable results to four typical inter-version baselines and the proposed CLAMI+ slightly improves the CLAMI method on average.

show abstract

A suite of metrics for quantifying historical changes to predict future change‐prone classes in object‐oriented software

Cited by 43 publications

References 36 publications

Applicability of Inter Project Validation for Determination of Change Prone Classes

Applicability of Inter Project Validation for Determination of Change Prone Classes

Developer-Related Factors in Change Prediction: An Empirical Assessment

Self-learning Change-prone Class Prediction

Contact Info

Product

Resources

About