Arsonists or Firefighters? Affectiveness in Agile Software Development

Destefanis, Giuseppe; Counsell, Steve; Swift, Stephen; Tonelli, Roberto; Marchesi, Michele

doi:10.1007/978-3-319-33515-5_12

Cited by 26 publications

(13 citation statements)

References 21 publications

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“…The mixed interaction 'polite-impolite' between developers could explain this fact. Ortu et al (2016) showed that when in the presence of impolite or negative comments, the probability of the next comment being impolite or negative was 13% and 25%, respectively. Hence part of the longer time could be spent in trying to shift the exchange of comments toward a (more) polite level.…”

Section: Discussionmentioning

confidence: 99%

Software development: do good manners matter?

Destefanis

Ortu

Counsell

et al. 2016

PeerJ Computer Science

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A successful software project is the result of a complex process involving, above all, people. Developers are the key factors for the success of a software development process, not merely as executors of tasks, but as protagonists and core of the whole development process. This paper investigates social aspects among developers working on software projects developed with the support of Agile tools. We studied 22 open-source software projects developed using the Agile board of the JIRA repository. All comments committed by developers involved in the projects were analyzed and we explored whether the politeness of comments affected the number of developers involved and the time required to fix any given issue. Our results showed that the level of politeness in the communication process among developers does have an effect on the time required to fix issues and, in the majority of the analysed projects, it had a positive correlation with attractiveness of the project to both active and potential developers. The more polite developers were, the less time it took to fix an issue.

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Section: Discussionmentioning

confidence: 99%

Software development: do good manners matter?

Destefanis

Ortu

Counsell

et al. 2016

PeerJ Computer Science

Self Cite

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“…Each contributor of the project can be represented by a node in the graph, and when contributor A comments a pull request created by contributor B, a link is generated from node A to node B. Such direct graph is called Issue Collaboration Network, and Figure 1 shows, as introduced by [29], [31], an example of such a network built using the data from Angular.js. The different colors in the graph represents communities of contributors (calculated using clustering algorithms), while the diameter of each node is proportional to the degree of the node.…”

Section: Methodsmentioning

confidence: 99%

How do you Propose Your Code Changes? Empirical Analysis of Affect Metrics of Pull Requests on GitHub

et al. 2020

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Software engineering methodologies rely on version control systems such as git to store source code artifacts and manage changes to the codebase. Pull requests include chunks of source code, history of changes, log messages around a proposed change of the mainstream codebase, and much discussion on whether to integrate such changes or not. A better understanding of what contributes to a pull request fate and latency will allow us to build predictive models of what is going to happen and when. Several factors can influence the acceptance of pull requests, many of which are related to the individual aspects of software developers. In this study, we aim to understand how the affect (e.g., sentiment, discrete emotions, and valence-arousaldominance dimensions) expressed in the discussion of pull request issues influence the acceptance of pull requests. We conducted a mining study of large git software repositories and analyzed more than 150,000 issues with more than 1,000,000 comments in them. We built a model to understand whether the affect and the politeness have an impact on the chance of issues and pull requests to be merged-i.e., the code which fixes the issue is integrated in the codebase. We built two logistic classifiers, one without affect metrics and one with them. By comparing the two classifiers, we show that the affect metrics improve the prediction performance. Our results show that valence (expressed in comments received and posted by a reporter) and joy expressed in the comments written by a reporter are linked to a higher likelihood of issues to be merged. On the contrary, sadness, anger, and arousal expressed in the comments written by a reporter, and anger, arousal, and dominance expressed in the comments received by a reporter, are linked to a lower likelihood of a pull request to be merged.

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“…In recent years, sentiment analysis is receiving increasing attention as part of human factors of SE [18] and has been widely applied in SE tasks [19][20][21][22][23][24][25][26][27][28]. A number of studies applied sentiment analysis in the collaborative online environment (e.g., GitHub, JIRA, Stack Overflow, and App store) presented as follows: Pletea et al [29] mined emotions from security-related discussions around commits and pull request on GitHub, and found that more negative emotions are expressed in securityrelated discussions than in other discussions.…”

Section: B Sentiment Analysis Application In Sementioning

confidence: 99%

“…These SE texts provides a valuable perspective for researchers to detect the developers' satisfaction or difficulties about the project, i.e., their positive or negative sentiments. Thus, to better support software engineering (e.g., [22]) and program comprehension (e.g., [25]) tasks, a growing body of work [19][20][21][22][23][24][25][26][27][28] applies automated sentiment analysis on SE texts from different online tools such as app stores [34][35], Stack Overflow [4,32,36], GitHub [29][30][31], and JIRA [21,22]. These analyses are also favorable in daily SE practice because unlike the traditional approaches [5,6,42], they do not need direct observations or interactions on the developers, thus not likely to hinder them from their assigned development tasks.…”

Section: Introductionmentioning

confidence: 99%

Exploiting the Unique Expression for Improved Sentiment Analysis in Software Engineering Text

Sun

Gao

Kuang

et al. 2021

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

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Sentiment analysis on software engineering (SE) texts has been widely used in the SE research, such as evaluating app reviews or analyzing developers' sentiments in commit messages. To better support the use of automated sentiment analysis for SE tasks, researchers built an SE-domain-specified sentiment dictionary to further improve the accuracy of the results. Unfortunately, recent work reported that current mainstream tools for sentiment analysis still cannot provide reliable results when analyzing the sentiments in SE texts. We suggest that the reason for this situation is because the way of expressing sentiments in SE texts is largely different from the way in social network or movie comments. In this paper, we propose to improve sentiment analysis in SE texts by using sentence structures, a different perspective from building a domain dictionary. Specifically, we use sentence structures to first identify whether the author is expressing her sentiment in a given clause of an SE text, and to further adjust the calculation of sentiments which are confirmed in the clause. An empirical evaluation based on four different datasets shows that our approach can outperform two dictionary-based baseline approaches, and is more generalizable compared to a learning-based baseline approach.

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Arsonists or Firefighters? Affectiveness in Agile Software Development

Cited by 26 publications

References 21 publications

Software development: do good manners matter?

Software development: do good manners matter?

How do you Propose Your Code Changes? Empirical Analysis of Affect Metrics of Pull Requests on GitHub

Exploiting the Unique Expression for Improved Sentiment Analysis in Software Engineering Text

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