Continuous delivery of cloud systems requires constant running of jobs (build processes, tests, etc.). One issue that plagues this continuous integration (CI) process are intermittent failures - non-deterministic, false alarms that do not result from a bug in the software or job specification, but rather from issues in the underlying infrastructure. At Mozilla, such intermittent failures are called oranges as a reference to the color of the build status indicator. As such intermittent failures disrupt CI and lead to failures, they erode the developers' trust in the jobs. We present a novel approach that automatically classifies failing jobs to determine whether job execution failures arise from an actual software bug or were caused by flakiness in the job (e.g., test) or the underlying infrastructure. For this purpose, we train classification models using job telemetry data to diagnose failure patterns involving features such as runtime, cpu load, operating system version, or specific platform with high precision. In an evaluation on a set of Mozilla CI jobs, our approach achieves precision scores of 73%, on average, across all data sets with some test suites achieving precision scores good enough for fully automated classification (i.e., precision scores of up to 100%), and recall scores of 82% on average (up to 94%).

Since its introduction 10 years ago, GIT has taken the world of version control systems (VCS) by storm. Its success is partly due to creating opportunities for new usage patterns that empower developers to work more efficiently. However, the resulting change in both user behavior and the way GIT stores changes impacts data mining and data analytics procedures [6], [13]. While some of these unique characteristics can be managed by adjusting mining and analytical techniques, others can lead to severe data loss and the inability to audit code changes, e.g. knowing the full history of changes of code related to security and privacy functionality. Thus, switching to GIT comes with challenges to established development process analytics. This paper is based on our experience in attempting to provide continuous process analysis for Microsoft product teams who switching to GIT as their primary VCS. We illustrate how GIT's concepts and usage patterns create a need for changing well-established data analytic processes. The goal of this paper is to raise awareness how certain GIT operations may damage or even destroy information about historical code changes necessary for continuous data development process analytics. To that end, we provide a list of common GIT usage patterns with a description of how these operations impact data mining applications. Finally, we provide examples of how one may counteract the effects of such destructive operations in the future. We further provide a new algorithm to detect integration paths that is specific to distributed version control systems like GIT, which allows us to reconstruct the information that is crucial to most development process analytics.

In a manual examination of more than 7,000 issue reports from the bug databases of five open-source projects, we found 33.8% of all bug reports to be misclassified - that is, rather than referring to a code fix, they resulted in a new feature, an update to documentation, or an internal refactoring. This misclassification introduces bias in bug prediction models, confusing bugs and features: On average, 39% of files marked as defective actually never had a bug. We discuss the impact of this misclassification on earlier studies and recommend manual data validation for future studies.

When analyzing version histories, researchers traditionally focused on single events: e.g. the change that causes a bug, the fix that resolves an issue. Sometimes however, there are indirect effects that count: Changing a module may lead to plenty of follow-up modifications in other places, making the initial change having an impact on those later changes. To this end, we group changes into change genealogies, graphs of changes reflecting their mutual dependencies and influences and develop new metrics to capture the spatial and temporal influence of changes. In this paper, we show that change genealogies offer good classification models when identifying defective source files: With a median precision of 73% and a median recall of 76%, change genealogy defect prediction models not only show better classification accuracies as models based on code complexity, but can also outperform classification models based on code dependency network metrics.

In software development, bug reports provide crucial information to developers. However, these reports widely differ in their quality. We conducted a survey among developers and users of APACHE, ECLIPSE, and MOZILLA to find out what makes a good bug report. The analysis of the 466 responses revealed an information mismatch between what developers need and what users supply. Most developers consider steps to reproduce, stack traces, and test cases as helpful, which are, at the same time, most difficult to provide for users. Such insight is helpful for designing new bug tracking tools that guide users at collecting and providing more helpful information. Our CUEZILLA prototype is such a tool and measures the quality of new bug reports; it also recommends which elements should be added to improve the quality. We trained CUEZILLA on a sample of 289 bug reports, rated by developers as part of the survey. The participants of our survey also provided 175 comments on hurdles in reporting and resolving bugs. Based on these comments, we discuss several recommendations for better bug tracking systems, which should focus on engaging bug reporters, better tool support, and improved handling of bug duplicates.

In software development, bug reports provide crucial information to developers. However, these reports widely differ in their quality. We conducted a survey among developers and users of APACHE, ECLIPSE, and MOZILLA to find out what makes a good bug report.

Developers typically rely on the information submitted by end-users to resolve bugs. We conducted a survey on information needs and commonly faced problems with bug reporting among several hundred developers and users of the APACHE, ECLIPSE and MOZILLA projects. In this paper, we present the results of a card sort on the 175 comments sent back to us by the responders of the survey. The card sort revealed several hurdles involved in reporting and resolving bugs, which we present in a collection of recommendations for the design of new bug tracking systems. Such systems could provide contextual assistance, reminders to add information, and most important, assistance to collect and report crucial information to developers.