What Software Engineering Can Learn from Research on Affect in Social Psychology

Gren, Lucas; Lenberg, Per; Ljungberg, Karolina

doi:10.1109/semotion.2019.00015

Cited by 3 publications

(2 citation statements)

References 44 publications

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“…Task context including the developers' perception (social) 32,33 as well as their development task (technical) 34,35 is an important aspect to comprehend and interpret software artifacts. Thus, socio-technical analysis combining the up-mentioned perspectives is performed on these artifacts.…”

Section: Socio-technical Analysis On Developersmentioning

confidence: 99%

Bug report priority prediction using social and technical features

Huang,

Shao,

Fan

et al. 2023

J Software Evolu Process

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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.

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Section: Socio-technical Analysis On Developersmentioning

confidence: 99%

Bug report priority prediction using social and technical features

Huang,

Shao,

Fan

et al. 2023

J Software Evolu Process

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“…Indeed, the unforeseen part of results shed light on the necessity of a deeper understanding of developers' sentiments and their impact on software community through qualitative and quantitative study. Beyond technical aspects, the research community need to improve and reshape the framework of comprehending developers' perception [17]- [19], [66] as well as their task context [16], [67]- [69]. Finding 3.…”

Section: B Rq2: Features' Predictive Powermentioning

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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