Predicting the emergence of community smells using socio-technical metrics: A machine-learning approach

Palomba, Fabio; Tamburri, Damian A.

doi:10.1016/j.jss.2020.110847

Cited by 32 publications

(9 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We apply the SCIKIT-LEARN package 60 from PYTHON to train machine learners using multiple classifiers that have been used in prior studies, 6,18,29,32,45,61 including KNN, random forest (RF), decision tree (DT), support vector machine (SVM), multilayer perceptron (MLP), Adaboost (ADA), Naive-Bayes (NB), and logistic regression (LR). The details of parameter tuning and classifier selection will be described in Section 5.2.…”

Section: Machine Learnersmentioning

confidence: 99%

Bug report priority prediction using social and technical features

Huang,

Shao,

Fan

et al. 2023

J Software Evolu Process

View full text Add to dashboard Cite

SummarySoftware stakeholders report bugs in issue tracking system (ITS) with manually labeled priorities. However, the lack of knowledge and standard for prioritization may cause stakeholders to mislabel the priorities. In response, priority predictors are actively developed to support them. Prior studies trained machine learners based on textual similarity, categorical, and numeric technical features of bug reports. Most models were validated by time‐insensitive approaches, and they were producing suboptimal results for practical usage. While they ignored the social aspects of ITS, the technical aspects were also limited in surface features of bug reports. To better model the bug report, we extract their topic and most similar code structures. Since ITS bridges users and developers as the main contributors, we also integrate their experience, sentiment, and socio‐technical features to construct a new dataset. Then, we perform two‐classed and multiclassed bug priority prediction based on the dataset. We also introduce adversarial training using generated training data with random word swap and random word deletion. We validate our model in within‐project, cross‐project, and time‐wise scenarios, and it outperforms the two baselines by up to 15% in area under curve‐receiver operating characteristics (AUC‐ROC) and 19% in Matthews correlation coefficient (MCC). We reveal involving contributor (i.e., assignee and reporter) features such as sentiment that could boost prediction performance. Finally, we test statistically the mean and distribution of the features that reflect the differences in social and technical aspects (e.g., quality of communication and resource distribution) between high and low priority reports. In conclusion, we suggest that researchers should consider both social and technical aspects of ITS in bug report priority prediction and introduce adversarial training to boost model performance.

show abstract

Section: Machine Learnersmentioning

confidence: 99%

Bug report priority prediction using social and technical features

Huang,

Shao,

Fan

et al. 2023

J Software Evolu Process

View full text Add to dashboard Cite

show abstract

“…In recent times, community smells are studied to incorporate the organizational and social aspects of the software development community in software engineering research. Some studies [1][6] [5] and predicting [9][10] [11] these smells in open-source projects. Besides, a few studies investigated the relationship and the impact of community smells on different software artifacts such as code smell and bug [2][13] [18].…”

Section: A Missing Link Community Smellmentioning

confidence: 99%

“…The enhanced tool detected both communities smells and code smells in an automated approach [7]. Besides detection, a few studies [9][10] [11] tried to predict the community smells. Palomba et al [9] worked on the prediction of community smells from socio-technical factors.…”

Section: A Missing Link Community Smellmentioning

confidence: 99%

“…Besides detection, a few studies [9][10] [11] tried to predict the community smells. Palomba et al [9] worked on the prediction of community smells from socio-technical factors. Almarimi et al [11] also built a model to predict community smells using Ensemble Classifier Chain (ECC) and Genetic Programming (GP) techniques.…”

Section: A Missing Link Community Smellmentioning

confidence: 99%

“…Some studies worked with the definition [1] [6], and detection [3] [7] of community smells, while others studied the diffuseness [5] and variability [8] of community smells. A few studies [9][10] [11] worked on the prediction of community smells. The effect of community smells on predicting the intensity of code smell [2] [12], and bug [13] is also studied.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Do Missing Link Community Smell Affect Developers Productivity: An Empirical Study

2021

View full text Add to dashboard Cite

Missing link smell occurs when developers contribute to the same source code without communicating with each other. Existing studies have analyzed the relationship of missing link smells with code smell and developer contribution. However, the productivity of developers involved in missing link smell has not been explored yet. This study investigates how productivity differs between smelly and non-smelly developers. For this purpose, the productivity of smelly and non-smelly developers of seven open-source projects are analyzed. The result shows that the developers not involved in missing link smell have more productivity than the developers involved in smells. The observed difference is also found statistically significant.

show abstract

Evolving software forges: An experience report from Apache Allura

Tamburri

Palomba

2021

J Software Evolu Process

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Predicting the emergence of community smells using socio-technical metrics: A machine-learning approach

Cited by 32 publications

References 42 publications

Bug report priority prediction using social and technical features

Bug report priority prediction using social and technical features

Do Missing Link Community Smell Affect Developers Productivity: An Empirical Study

Evolving software forges: An experience report from Apache Allura

Contact Info

Product

Resources

About