Security and emotion: sentiment analysis of security discussions on GitHub

Abstract-Human Affectiveness, i.e., the emotional state of a person, plays a crucial role in many domains where it can make or break a team's ability to produce successful products. Software development is a collaborative activity as well, yet there is little information on how affectiveness impacts software productivity. As a first measure of this impact, this paper analyzes the relation between sentiment, emotions and politeness of developers in more than 560K Jira comments with the time to fix a Jira issue. We found that the happier developers are (expressing emotions such as JOY and LOVE in their comments), the shorter the issue fixing time is likely to be. In contrast, negative emotions such as SADNESS, are linked with longer issue fixing time. Politeness plays a more complex role and we empirically analyze its impact on developers' productivity.

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“…This three metrics have been used by other researchers, i.e., politeness [23] and [24], sentiment [25] and [26], and emotion [9].…”

Section: Introductionmentioning

confidence: 99%

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

Ortu

Adams

Destefanis

et al. 2015

2015 IEEE/ACM 12th Working Conference on Mining Software Repositories

126

159

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“…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%

“…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%

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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“…The information needs solely identified by the authors are confirmed with the managers by presenting them the information needs along with the example analysts' comments where such an information is asked. Comment annotation using keywords-based approach: Ground truth for every information need is established using a keyword-based approach [52]. A list of keywords corresponding to each information need is prepared iteratively.…”

Section: Ground Truthmentioning

confidence: 99%

Reducing user input requests to improve IT support ticket resolution process

et al. 2017

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Management and maintenance of IT infrastructure resources such as hardware, software and network is an integral part of software development and maintenance projects. Service management ensures that the tickets submitted by users, i.e. software developers, are serviced within the agreed resolution times. Failure to meet those times induces penalty on the service provider. To prevent a spurious penalty on the service provider, non-working hours such as waiting for user inputs are not included in the measured resolution time, that is, a service level clock pauses its timing. Nevertheless, the user interactions slow down the resolution process, that is, add to user experienced resolution time and degrade user experience. Therefore, this work is motivated by the need to analyze and reduce user input requests in tickets' life cycle.To address this problem, we analyze user input requests and investigate their impact on user experienced resolution time. We distinguish between input requests of two types: real, seeking information from the user to process the ticket and tactical, when no information is asked but the user input request is raised merely to pause the service level clock. Next, we propose a system that preempts a user at the time of ticket submission to provide additional information that the analyst, a person responsible for servicing the ticket, is likely to ask, thus reducing real user input requests. Further, we propose a detection system to identify tactical user input requests.To evaluate the approach, we conducted a case study in a large global IT company. We observed that around 57% of the tickets have user input requests in the life cycle, causing user experienced resolution time to be almost twice as long as the measured service resolution time. The proposed preemptive system preempts the information needs with an average accuracy of 94-99% across five cross validations while traditional approaches such as logistic regression and naive Bayes have accuracy in the range of 50-60%. The detection system identifies around 15% of the total user input requests as tactical. Therefore, the proposed solution can efficiently bring down the number of user input requests and, hence, improve the user-experienced resolution time.

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Security and emotion: sentiment analysis of security discussions on GitHub

Cited by 151 publications

References 8 publications

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

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

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

Reducing user input requests to improve IT support ticket resolution process

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