Are Bullies More Productive? Empirical Study of Affectiveness vs. Issue Fixing Time

Ortu, Marco; Adams, Bram; Destefanis, Giuseppe; Tourani, Parastou; Marchesi, Michele; Tonelli, Roberto

doi:10.1109/msr.2015.35

Cited by 126 publications

(168 citation statements)

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“…A growing body of literature has investigated the importance and the influence of human and social aspects in software engineering and software development , 2015a,b, Müller and Fritz 2015, Ortu et al 2016b) and how several factor, e.g., politeness (Destefanis et al , 2017 affect developers' productivity. Rigby and Hassan (2007) analyzed, using a psychometrically-based linguistic analysis tool the five big personality traits of software developers in the Apache httpd server mailing list.…”

Section: Affect Emotions Sentiment Analysis and Developer Personalimentioning

confidence: 99%

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How diverse is your team? Investigating gender and nationality diversity in GitHub teams

Destefanis

Counsell

Swift

et al. 2017

J Softw Eng Res Dev

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Background:Building an effective team of developers is a complex task faced by both software companies and open source communities. The problem of forming a "dream" team involves many variables, including consideration of human factors and it is not a dilemma solvable in a mathematical way. Empirical studies might provide interesting insights to explain which factors need to be taken into account in building a team of developers and which levers act to optimise productivity among developers. Aim: In this paper, we present the results of an empirical study aimed at investigating the link between team diversity (i.e., gender, nationality) and productivity (issue fixing time). Method: We consider issues solved from the GHTorrent dataset inferring gender and nationality of each team's members. We also evaluate the politeness of all comments involved in issue resolution. Results: Results show that higher gender diversity is linked with a lower team average issue fixing time (higher productivity), that nationality diversity is linked with lower team politeness and that gender diversity is linked with higher sentiment.

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Section: Affect Emotions Sentiment Analysis and Developer Personalimentioning

confidence: 99%

“…Politeness, along with other affective metrics, i.e., sentiment and emotions, has been used in several studies in software engineering Guzman et al 2014;Ortu et al 2015a, b;2016a;Pletea et al 2014). In what follows, we briefly introduce politeness and the methodology used to infer GitHub development teams.…”

Section: Introductionmentioning

confidence: 99%

How diverse is your team? Investigating gender and nationality diversity in GitHub teams

Destefanis

Counsell

Swift

et al. 2017

J Softw Eng Res Dev

Self Cite

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“…Therefore, next we address RQ3 and discuss a simple setup of a study aiming at understanding differences in response times for positive, neutral and negative texts. While we do not aim at replicating an existing study, we note that similar questions have been considered in the literature [11], [12].…”

Section: Impact Of the Choice Of Sentiment Analysis Toolmentioning

confidence: 99%

“…Sentiment polarity analysis has been recently applied in the software engineering context to study commit comments in GitHub [9], GitHub discussions related to security [10], productivity in Jira issue resolution [11], activity of contributors in Gentoo [12] and evolution of developers' sentiments in the openSUSE Factory [13]. It has also been suggested when assessing technical candidates on the social web [14].…”

Section: Introductionmentioning

confidence: 99%

“…It has also been suggested when assessing technical candidates on the social web [14]. Not surprisingly, all the aforementioned software engineering studies reuse the existing sentiment polarity tools, e.g., Guzman et al [15], [9] and Rousinopoulos et al [13] use NLTK, while Garcia et al [12], Pletea et al [10] and Ortu et al [11] opted for SentiStrength. While the reuse of the existing tools facilitated the application of the sentiment polarity analysis techniques in the software engineering domain, it also introduced a commonly recognized threat to validity of the results obtained: those tools have been trained on nonsoftware engineering related texts such as movie reviews or product reviews and might misidentify (or fail to identify) polarity of a sentiment in a software engineering artefact such as a commit comment [9], [10].…”

Section: Introductionmentioning

confidence: 99%

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Choosing your weapons: On sentiment analysis tools for software engineering research

Jongeling

Datta

Serebrenik

2015

2015 IEEE International Conference on Software Maintenance and Evolution (ICSME)

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Abstract-Recent years have seen an increasing attention to social aspects of software engineering, including studies of emotions and sentiments experienced and expressed by the software developers. Most of these studies reuse existing sentiment analysis tools such as SentiStrength and NLTK. However, these tools have been trained on product reviews and movie reviews and, therefore, their results might not be applicable in the software engineering domain.In this paper we study whether the sentiment analysis tools agree with the sentiment recognized by human evaluators (as reported in an earlier study) as well as with each other. Furthermore, we evaluate the impact of the choice of a sentiment analysis tool on software engineering studies by conducting a simple study of differences in issue resolution times for positive, negative and neutral texts. We repeat the study for seven datasets (issue trackers and STACK OVERFLOW questions) and different sentiment analysis tools and observe that the disagreement between the tools can lead to contradictory conclusions.

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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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Are Bullies More Productive? Empirical Study of Affectiveness vs. Issue Fixing Time

Cited by 126 publications

References 27 publications

How diverse is your team? Investigating gender and nationality diversity in GitHub teams

How diverse is your team? Investigating gender and nationality diversity in GitHub teams

Choosing your weapons: On sentiment analysis tools for software engineering research

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

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