Predicting bug-fixing time: An empirical study of commercial software projects

Zhang, Hongyu; Gong, Liang; Versteeg, Steve

doi:10.1109/icse.2013.6606654

Cited by 136 publications

(107 citation statements)

References 22 publications

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“…We used a dichotomic variable for the IFT, since by analyzing the distribution of IFTs we found that in line with previous studies (Zhang et al 2013), they are distributed as a long-tail distribution (power law). Variables with a logarithmic distribution do not fit well with linear models due to their large variations; this led us to use a logistic regression model, since it fit better with logarithmic distributions.…”

Section: Team Productivitysupporting

confidence: 62%

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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: Team Productivitysupporting

confidence: 62%

“…This renders team Productivity as a binary variable. The approach has already been demonstrated successfully in and Zhang et al (2013).…”

Section: Experimental Designmentioning

confidence: 98%

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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“…• consider a↵ects in models for bug fixing time estimation [23]. Ortu et al [12] showed that bug fixing time correlate with the a↵ects expressed by developers in issue comments.…”

Section: Research Opportunitiesmentioning

confidence: 99%

The emotional side of software developers in JIRA

Ortu

Murgia

Destefanis

et al. 2016

Proceedings of the 13th International Conference on Mining Software Repositories

104

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Issue tracking systems store valuable data for testing hypotheses concerning maintenance, building statistical prediction models and (recently) investigating developer a↵ec-tiveness. For the latter, issue tracking systems can be mined to explore developers emotions, sentiments and politeness -a↵ects for short. However, research on a↵ect detection in software artefacts is still in its early stage due to the lack of manually validated data and tools.In this paper, we contribute to the research of a↵ects on software artefacts by providing a labeling of emotions present on issue comments. We manually labeled 2,000 issue comments and 4,000 sentences written by developers with emotions such as love, joy, surprise, anger, sadness and fear. Labeled comments and sentences are linked to software artefacts reported in our previously published dataset (containing more than 1K projects, more than 700K issue reports and more than 2 million issue comments). The enriched dataset presented in this paper allows the investigation of the role of a↵ects in software development.

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“…For example, severity identification [14] targets to identify the status of bug reports for further scheduling in bug management; time expectation of bugs [29] models the time cost of bug deception and expects the time rate of specified bug reports; reopened-bug analysis [20], [30] finds the wrongly fixed bug reports to evade deferring the software release.…”

Section: ) Bug Triagementioning

confidence: 99%

Bug Triage Using Dimensionality Reduction Technique And PSO Algorithm

Amritha¹,

Rani²

2016

IJECS

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Abstract-The process of fixing bug is bug triage that aims to properly assign a developer to a new bug. Software companies pay out most of their expenses in dealing with these bugs. To reduce time and cost of bug triaging, an automated approach is developed to predict a developer with relevant experience to solve the new coming report. In proposed approach data reduction is done on bug data set which will reduce the scale of the data as well as increase the quality of the data. Instance selection and feature selection is also used simultaneously with historical bug data. Previously, text classification techniques are applied to conduct bug triage. The problem here is to get quality bug data sets as they are of very huge in size. In the proposed system, the problems of reducing the size and to improve the quality of bug data are addressed.First, pre-processing is done to the remove unimportant attributes and to identify missing terms. Then instance selection is combined with feature selection by using Dimensionality reduction technique to simultaneously reduce data size on the bug dimension and the word dimension. By using PSO algorithm, the reduction order is determined using fitness value. It is used to produce quality bug data set. The results show that the proposed system can effectively reduce the data size and improve the accuracy of bug triage. The proposed system provides an approach to leveraging techniques on data processing to form reduced and high eminence bug data in software improvement and maintenance.

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Predicting bug-fixing time: An empirical study of commercial software projects

Cited by 136 publications

References 22 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

The emotional side of software developers in JIRA

Bug Triage Using Dimensionality Reduction Technique And PSO Algorithm

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