A Developer Centered Bug Prediction Model

Nucci, Dario Di; Palomba, Fabio; Rosa, Giuseppe De; Bavota, Gabriele; Oliveto, Rocco; Lucia, Andrea De

doi:10.1109/tse.2017.2659747

Cited by 104 publications

(89 citation statements)

References 38 publications

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“…In particular, we observed that the best performance is achieved by the model defined by Di Nucci et al [19]. When compared to the model exploiting the evolution metrics devised by Elish et al, DCBM still produces better performances.…”

Section: Introductionmentioning

confidence: 62%

“…A consistent body of research analyzed the factors influencing the phenomenon [8], [9], [10], [11], [6], while others focused on understanding the role of product and evolution metrics to predict the future change-proneness of classes [22], [20], [19], [23], [17]. Since this paper is about change prediction models, in the following we summarize the related literature on previous research in this branch.…”

Section: Related Workmentioning

confidence: 99%

“…Besides the evolution metrics defined by Elish et al [17] and Girba et al [41], in this paper we also analyzed the role of developer-related factors that have been shown to be relevant for prediction purpose in other contexts [19].…”

Section: Related Workmentioning

confidence: 99%

“…2) The Developer Changes Based Model (DCBM) proposed by Di Nucci et al [19]. It employs the structural and semantic scattering of the developers that worked on a code element in a time window of size α as predictors.…”

Section: Empirical Study Definition and Designmentioning

confidence: 99%

“…In this paper, we aim at verifying our conjecture by studying the role of the metrics measuring the complexity of the development process in change prediction. In our study we investigated three prediction models previously defined in literature each one based on metrics that capture the complexity of the development process under different perspectives, i.e., (i) the Basic Code Change Model (BCCM) proposed by Hassan [18] which relies on the entropy of changes applied by developers, (ii) the Developer Changes Based Model (DCBM) devised by Di Nucci et al [19] that considers to what extent developers apply scattered changes in the system, and (iii) the Developer Model (DM) proposed by Bell et al [20] which analyzes how many developers touched a code element over time.…”

Section: Introductionmentioning

confidence: 99%

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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: Introductionmentioning

confidence: 62%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Empirical Study Definition and Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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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An extensive evaluation of ensemble techniques for software change prediction

Catolino

Ferrucci

2019

J Software Evolu Process

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Predicting the areas of the source code having a higher likelihood to change in the future represents an important activity to allow developers to plan preventive maintenance operations.For this reason, several change prediction models have been proposed. Moreover, research community demonstrated how different classifiers impact on the performance of devised models as well as classifiers tend to perform similarly even though they are able to correctly predict the change proneness of different code elements, possibly indicating the presence of some complementarity among them. In this paper, we deeper investigated whether the use of ensemble approaches, ie, machine learning techniques able to combine multiple classifiers, can improve the performances of change prediction models. Specifically, we built three change prediction models based on different predictors, ie, product-, process-metrics-, and developer-related factors, comparing the performances of four ensemble techniques (ie, Boosting, Random Forest, Bagging, and Voting) with those of standard machine learning classifiers (ie, Logistic Regression, Naive Bayes, Simple Logistic, and Multilayer Perceptron). The study was conducted on 33releases of 10 open-source systems, and the results showed how ensemble methods and in particular Random Forest provide a significant improvement of more than 10% in terms of F measure. Indeed, the statistical analyses conducted confirm the superiority of this ensemble technique. Moreover, the model built using developer-related factors performed better than the other models that exploit product and process metrics and achieves an overall median of F measure around 77%.

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Evolving software forges: An experience report from Apache Allura

Tamburri

Palomba

2021

J Software Evolu Process

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The open‐source phenomenon has reached unimaginable proportions to a point in which it is virtually impossible to find large applications that do not rely on open‐source as well. However, such proportions may turn into a risk if the organizational and socio‐technical aspects (e.g., the contribution and release schemes) behind open‐source communities are not explicitly supported by open‐source forges by‐design. In an effort to make such aspects explicit and supported by‐design in open‐source forges, we conducted empirical software engineering as follows: (a) Through online industrial surveying, we elicited organizational and social aspects relevant in open‐source communities; (b) through action research, we extended a widely known open‐source support system and top‐level Apache project Allura; (c) through ethnography, we studied the Allura community, and learning from its social and organizational structure, (d) we elicited a metrics framework that support more explicit organizational and socio‐technical design principles around open‐source communities. This article is an experience report on these results and the lessons we learned in obtaining them. We found that the extensions provided to Apache Allura formed the basis for community awareness by design, providing valuable and usable community characteristics. Ultimately, however, the extensions we provided to Apache Allura were deactivated by its core developers because of performance overheads. Our results and lessons learned allow us to provide recommendations for designing forges, like Github. Architecting a forge is a participatory process that requires active engagement, hence remarking the need for mechanisms enabling it. At the same time, we conclude that a more active support for the governance is required to avoid the failure of the forge.

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A Developer Centered Bug Prediction Model

Cited by 104 publications

References 38 publications

Developer-Related Factors in Change Prediction: An Empirical Assessment

Developer-Related Factors in Change Prediction: An Empirical Assessment

An extensive evaluation of ensemble techniques for software change prediction

Evolving software forges: An experience report from Apache Allura

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