An empirical study of dormant bugs

Chen, Tse-Hsun; Nagappan, Meiyappan; Shihab, Emad; Hassan, Ahmed E.

doi:10.1145/2597073.2597108

Cited by 64 publications

(23 citation statements)

References 51 publications

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“…We conducted several qualitative analysis in our RQs; however, it is impossible to manually study all revisions. To minimize the bias when conducting our qualitative analysis, we took statistically representative samples of all relevant revisions with a 95% confidence level and a 5% confidence interval [13] as what was done in prior studies [22], [23] (we ended up studying 384 randomly sampled revisions in RQ2 and 369 randomly sampled unaccepted revisions in RQ3).…”

Section: Threats To Validitymentioning

confidence: 99%

How Do Users Revise Answers on Technical Q&A Websites? A Case Study on Stack Overflow

Chen

Hassan

2020

IIEEE Trans. Software Eng.

Self Cite

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To ensure the quality of its shared knowledge, Stack Overflow encourages users to revise answers through a badge system, which is based on quantitative measures (e.g., a badge is awarded after revising more than 500 answers). Prior studies show that badges can positively steer the user behavior on Stack Overflow (e.g., increasing user participation). However, little is known whether revision-related badges have a negative impact on the quality of revisions since some studies show that certain users may game incentive systems to gain rewards. In this study, we analyze 3,871,966 revision records that are collected from 2,377,692 Stack Overflow answers. We find that: 1) Users performed a much larger than usual revisions on the badge-awarding days compared to normal days; 25% of the users did not make any more revisions once they received their first revision-related badge. 2) Performing more revisions than usual in a single day increased the likelihood of such revisions being rolled back (e.g., due to undesired or incorrect revisions). 3) Users were more likely to perform text and small revisions if they performed many revisions in a single day. Our findings are concurred by the Stack Overflow community, and they highlight the need for changes to the current badge system in order to provide a better balance between the quality and quantity of revisions.

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Section: Threats To Validitymentioning

confidence: 99%

How Do Users Revise Answers on Technical Q&A Websites? A Case Study on Stack Overflow

Chen

Hassan

2020

IIEEE Trans. Software Eng.

Self Cite

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“…Therefore, the time period of the test set is often short, e.g., a few days or months. However, bugs typically take years to be discovered and fixed [27]- [29]. Therefore, at the prediction time t predict , many buggy changes in the training set, especially the changes committed close to time t predict , would not have been found and fixed yet.…”

Section: B Time Sensitive Change Classificationmentioning

confidence: 99%

Online Defect Prediction for Imbalanced Data

Tan

Dara³

et al. 2015

2015 IEEE/ACM 37th IEEE International Conference on Software Engineering

228

198

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Abstract-Many defect prediction techniques are proposed to improve software reliability. Change classification predicts defects at the change level, where a change is the modifications to one file in a commit. In this paper, we conduct the first study of applying change classification in practice.We identify two issues in the prediction process, both of which contribute to the low prediction performance. First, the data are imbalanced-there are much fewer buggy changes than clean changes. Second, the commonly used cross-validation approach is inappropriate for evaluating the performance of change classification. To address these challenges, we apply and adapt online change classification, resampling, and updatable classification techniques to improve the classification performance.We perform the improved change classification techniques on one proprietary and six open source projects. Our results show that these techniques improve the precision of change classification by 12.2-89.5% or 6.4-34.8 percentage points (pp.) on the seven projects. In addition, we integrate change classification in the development process of the proprietary project. We have learned the following lessons: 1) new solutions are needed to convince developers to use and believe prediction results, and prediction results need to be actionable, 2) new and improved classification algorithms are needed to explain the prediction results, and insensible and unactionable explanations need to be filtered or refined, and 3) new techniques are needed to improve the relatively low precision.

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“…6,7 The Plug-in Development Environment (PDE) provides tools to create, develop, test, debug, build and deploy Eclipse plug-ins, fragments, features, update sites and RCP products. 8 The Eclipse Platform defines the set of frameworks and common services that collectively make up infrastructure required to support the use of Eclipse.…”

Section: Subject Systemsmentioning

confidence: 99%

“…However, in our study it is the time period between T R and T F . The timestamps of bug introduction (T I ) and bug reporting (T R ) can be certainly different, since a bug can remain dormant for a long time [7]. Although there are some algorithms [14] to identify bug introducing changes, it is difficult to map those changes to the associated bug reports.…”

Section: Terms and Metricsmentioning

confidence: 99%