Bora Caglayan

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Many corporate code developers are the beta testers of open source software.They continue testing until they are sure that they have a stable version to build their code on. In this respect defect predictors play a critical role to identify defective parts of the software. Performance of a defect predictor is determined by correctly finding defective parts(More)
Defect prediction models presented in the literature lack generalization unless the original study can be replicated using new datasets and in different organizational settings. Practitioners can also benefit from replicating studies in their own environment by gaining insights and comparing their findings with those reported. In this work, we replicated an(More)
We present an integrated measurement and defect prediction tool: <i>Dione</i>. Our tool enables organizations to measure, monitor, and control product quality through learning based defect prediction. Similar existing tools either provide data collection and analytics, or work just as a prediction engine. Therefore, companies need to deal with multiple(More)
<b>Background:</b> Reopened issues may cause problems in managing software maintenance effort. In order to take actions that will reduce the likelihood of issue reopening the possible causes of bug reopens should be analysed. <b>Aims:</b> In this paper, we investigate potential factors that may cause issue reopening. <b>Method:</b> We have extracted issue(More)
Developer teams may naturally emerge independent of managerial decisions, organizational structure, or work locations in large software. Such self organized collaboration teams of developers can be traced from the source code repositories. In this paper, we identify the developer teams in the collaboration network in order to present the work team evolution(More)
Software effort estimation is critical for resource allocation and planning. Accurate estimates enable managers to distribute the workload among resources in a balanced manner. The actual workload of developers may be different from the values observed in project management tools. In this research, we provide a summary of our experiences regarding: a)(More)
The aim of the research is to explore the impact of cognitive biases and social networks in testing and developing software. The research will aim to address two critical areas: i) to predict defective parts of the software, ii) to determine the right person to test the defective parts of the software. Every phase in software development requires analytical(More)
Test managers use intelligent predictors to increase testing efficiency and to decide on when to stop testing. However, those predictors would be impractical to use in an industry setting, unless measurement and prediction processes are automated. Prest as an open source tool aims to address this problem. Compared to other open source prediction and(More)
Defect prediction has been evolved with variety of metric sets, and defect types. Researchers found code, churn, and network metrics as significant indicators of defects. However, all metric sets may not be informative for all defect categories such that only one metric type may represent majority of a defect category. Our previous study showed that defect(More)
Background: Most of the defect prediction models are built for two purposes: 1) to detect defective and defect-free modules (binary classification), and 2) to estimate the number of defects (regression analysis). It would also be useful to give more information on the nature of defects so that software managers can plan their testing resources more(More)