On the detection of community smells using genetic programming-based ensemble classifier chain

Almarimi, Nuri; Ouni, Ali; Chouchen, Moataz; Saidani, Islem; Mkaouer, Mohamed Wiem

doi:10.1145/3372787.3390439

Cited by 24 publications

(26 citation statements)

References 45 publications

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“…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%

“…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 3 more Smart Citations

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

2021

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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.

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Section: A Missing Link Community Smellmentioning

confidence: 99%

Section: A Missing Link Community Smellmentioning

confidence: 99%

Section: A Missing Link Community Smellmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

2021

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“…Community smells were evaluated and predicted at the granularity of community sub-groups [1], [3], [8]- [11]. Differently, restructuring smelly communities rely on social efforts of every community member [12].…”

Section: Introductionmentioning

confidence: 99%

Predicting Community Smells’ Occurrence on Individual Developers by Sentiments

Huang

Shao

Fan

et al. 2021

2021 IEEE/ACM 29th International Conference on Program Comprehension (ICPC)

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Community smells appear in sub-optimal software development community structures, causing unforeseen additional project costs, e.g., lower productivity and more technical debt. Previous studies analyzed and predicted community smells in the granularity of community sub-groups using socio-technical factors. However, refactoring such smells requires the effort of developers individually. To eliminate them, supportive measures for every developer should be constructed according to their motifs and working states. Recent work revealed developers' personalities could influence community smells' variation, and their sentiments could impact productivity. Thus, sentiments could be evaluated to predict community smells' occurrence on them. To this aim, this paper builds a developer-oriented and sentiment-aware community smell prediction model considering 3 smells such as Organizational Silo, Lone Wolf, and Bottleneck. Furthermore, it also predicts if a developer quitted the community after being affected by any smell. The proposed model achieves cross-and within-project prediction F-Measure ranging from 76% to 93%. Research also reveals 6 sentimental features having stronger predictive power compared with activeness metrics. Imperative and indicative expressions, politeness, and several emotions are the most powerful predictors. Finally, we test statistically the mean and distribution of sentimental features. Based on our findings, we suggest developers should communicate in a straightforward and polite way.

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Improving the detection of community smells through socio‐technical and sentiment analysis

Almarimi

Ouni

Chouchen

et al. 2022

J Software Evolu Process

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Open source software development is regarded as a collaborative activity in which developers interact to build a software product. Such a human collaboration is described as an organized effort of the "social" activity of organizations, individuals, and stakeholders, which can affect the development community and the open source project health. Negative effects of the development community manifest typically in the form of community smells, which represent symptoms of organizational and social issues within the open source software development community that often lead to additional project costs and reduced software quality. Recognizing the advantages of the early detection of potential community smells in a software project, we introduce a novel approach that learns from various community organizational, social, and emotional aspects to provide an automated support for detecting community smells. In particular, our approach learns from a set of interleaving organizationalsocial and emotional symptoms that characterize the existence of community smell instances in a software project. We build a multi-label learning model to detect 10 common types of community smells. We use the ensemble classifier chain (ECC) model that transforms multi-label problems into several single-label problems, which are solved using genetic programming (GP) to find the optimal detection rules for each smell type. To evaluate the performance of our approach, we conducted an empirical study on a benchmark of 143 open source projects. The statistical tests of our results show that our approach can detect community smells with an average Fmeasure of 93%, achieving a better performance compared to different state-of-theart techniques. Furthermore, we investigate the most influential community-related metrics to identify each community smell type.human factors in software engineering, software organization and properties | INTRODUCTIONOpen source software (OSS) development is a collaborative activity in which developers interact between each other to develop and maintain a software system. 1,2 Nowadays, OSS is becoming an essential part in the software economy. The number of OSS projects and their size has been growing at exponential rate. 3 The success of OSS development leads to building sustainable software systems. 2,4,5 Unlike proprietary software, OSS projects can be developed and maintained by the whole community, providing more transparency on the source code, development processes, and technologies as well as developers communications. As there exist a substantial number of OSS projects, there is a continuous

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On the detection of community smells using genetic programming-based ensemble classifier chain

Cited by 24 publications

References 45 publications

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

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

Predicting Community Smells’ Occurrence on Individual Developers by Sentiments

Improving the detection of community smells through socio‐technical and sentiment analysis

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