TY - GEN
T1 - Bug-fix time prediction models
T2 - 8th Working Conference on Mining Software Repositories, MSR 2011, Co-located with ICSE 2011
AU - Bhattacharya, Pamela
AU - Neamtiu, Iulian
PY - 2011
Y1 - 2011
N2 - Predicting bug-fix time is useful in several areas of software evolution, such as predicting software quality or coordinating development effort during bug triaging. Prior work has proposed bug-fix time prediction models that use various bug report attributes (e.g., number of developers who participated in fixing the bug, bug severity, number of patches, bug-opener's reputation) for estimating the time it will take to fix a newly-reported bug. In this paper we take a step towards constructing more accurate and more general bug-fix time prediction models by showing how existing models fail to validate on large projects widely-used in bug studies. In particular, we used multivariate and univariate regression testing to test the prediction significance of existing models on 512,474 bug reports from five open source projects: Eclipse, Chrome and three products from the Mozilla project (Firefox, Seamonkey and Thunderbird). The results of our regression testing indicate that the predictive power of existing models is between 30% and 49% and that there is a need for more independent variables (attributes) when constructing a prediction model. Additionally, we found that, unlike in prior recent studies on commercial software, in the projects we examined there is no correlation between bug-fix likelihood, bug-opener's reputation and the time it takes to fix a bug. These findings indicate three open research problems: (1) assessing whether prioritizing bugs using bug-opener's reputation is beneficial, (2) identifying attributes which are effective in predicting bug-fix time, and (3) constructing bug-fix time prediction models which can be validated on multiple projects.
AB - Predicting bug-fix time is useful in several areas of software evolution, such as predicting software quality or coordinating development effort during bug triaging. Prior work has proposed bug-fix time prediction models that use various bug report attributes (e.g., number of developers who participated in fixing the bug, bug severity, number of patches, bug-opener's reputation) for estimating the time it will take to fix a newly-reported bug. In this paper we take a step towards constructing more accurate and more general bug-fix time prediction models by showing how existing models fail to validate on large projects widely-used in bug studies. In particular, we used multivariate and univariate regression testing to test the prediction significance of existing models on 512,474 bug reports from five open source projects: Eclipse, Chrome and three products from the Mozilla project (Firefox, Seamonkey and Thunderbird). The results of our regression testing indicate that the predictive power of existing models is between 30% and 49% and that there is a need for more independent variables (attributes) when constructing a prediction model. Additionally, we found that, unlike in prior recent studies on commercial software, in the projects we examined there is no correlation between bug-fix likelihood, bug-opener's reputation and the time it takes to fix a bug. These findings indicate three open research problems: (1) assessing whether prioritizing bugs using bug-opener's reputation is beneficial, (2) identifying attributes which are effective in predicting bug-fix time, and (3) constructing bug-fix time prediction models which can be validated on multiple projects.
KW - bug report triage
KW - bug-fix time
KW - issue tracking
KW - statistical model
UR - http://www.scopus.com/inward/record.url?scp=79959229989&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=79959229989&partnerID=8YFLogxK
U2 - 10.1145/1985441.1985472
DO - 10.1145/1985441.1985472
M3 - Conference contribution
AN - SCOPUS:79959229989
SN - 9781450305747
T3 - Proceedings - International Conference on Software Engineering
SP - 207
EP - 210
BT - MSR'11
Y2 - 21 May 2011 through 22 May 2011
ER -