Splicing Community Patterns and Smells

Stefano, Manuel De; Pecorelli, Fabiano; Tamburri, Damian A.; Palomba, Fabio; Lucia, Andrea De

doi:10.1145/3387940.3392204

Cited by 19 publications

(11 citation statements)

References 34 publications

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“…Furthermore, the authors observed that community smells could be the preventers of refactoring, and they also intensify code smells continuously [3]. Alternatively, the authors also discovered the associations between community patterns [36], e.g., Formal Group, and Informal Network, to specific community smells.…”

Section: A Community Smellmentioning

confidence: 97%

“…Tamburri, Palomba et al contributed a series of researches [1], [3], [8], [9], [12], [14], [35], [36] concerning the definition [1], [9], detection [1], diffuseness [1], [35], and variability [14] of community smells, as well as their impact on software maintainability [3]. The authors treat community smells as patterns of motifs over collaboration and communication graphs, and they implemented a detection tool called CODE-FACE4SMELLS by extending a socio-technical analysis tool called CODEFACE.…”

Section: A Community Smellmentioning

confidence: 99%

See 1 more Smart Citation

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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Section: A Community Smellmentioning

confidence: 97%

Section: A Community Smellmentioning

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)

View full text Add to dashboard Cite

show abstract

“…Similarly, a number of researchers have attempted to identify relations between sociotechnical aspects and properties of source code like defect-proneness, [53][54][55][56] change-proneness, [57][58][59] continuous integration build failures, 60,61 and long-term sustainability of software communities. [62][63][64] A complementary view on software health concerns with the analysis of open-source projects and their characteristics. In this respect, a vast body of knowledge has been reported and synthetized through systematic literature reviews.…”

Section: Related Workmentioning

confidence: 99%

Evolving software forges: An experience report from Apache Allura

Tamburri

Palomba

2021

J Software Evolu Process

Self Cite

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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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“…Also, research on community smells has basically focused on four community smells, Organizational Silo, Black Cloud, Lone Wolf, and Radio Silence/Bottleneck, which have impact in real scenarios [10,11,12,13,14,15,16]. These studies analyzed these community smells and their impact on coordination, communication, and cooperation, which are critical factors for successful teamwork in software development teams.…”

Section: Introductionmentioning

confidence: 99%