Duplicate bug report detection using clustering

Bug reporting and fixing the reported bugs play a critical part in the development and maintenance of software systems. The software developers and end users can collaborate in this process to improve the reliability of software systems. Various end users report the defects they have found in the software and how these bugs affect them. However, the same defect may be reported independently by several users leading to a significant number of duplicate bug reports. There are a number of existing methods for detecting duplicate bug reports, but the best results so far account for only 24% of actual duplicates. In this paper, we propose a new method based on clustering to identify a larger proportion of duplicate bug reports while keeping the false positives of misidentified non-duplicates low. The proposed approach is experimentally evaluated on a large sample of bug reports from three public domain data sets. The results show that this approach achieves better performance in terms of a harmonic measure that combines true positive and true negative rates when compared to the existing methods

Data-Driven Application Maintenance: Views from the Trenches

In this paper we present our experience during design, development, and pilot deployments of a data-driven machine learning based application maintenance solution. We implemented a proof of concept to address a spectrum of interrelated problems encountered in application maintenance projects including duplicate incident ticket identification, assignee recommendation, theme mining, and mapping of incidents to business processes. In the context of IT services, these problems are frequently encountered, yet there is a gap in bringing automation and optimization. Despite long-standing research around mining and analysis of software repositories, such research outputs are not adopted well in practice due to the constraints these solutions impose on the users. We discuss need for designing pragmatic solutions with low barriers to adoption and addressing right level of complexity of problems with respect to underlying business constraints and nature of data.Comment: Earlier version of paper appearing in proceedings of the 4th International Workshop on Software Engineering Research and Industrial Practice (SER&IP), IEEE Press, pp. 48-54, 201

Leveraging the Power of Crowds: Automated Test Report Processing for The Maintenance of Mobile Applications

Crowdsourcing is an emerging distributed problem-solving model combining human and machine computation. It collects intelligence and knowledge from a large and diverse workforce to complete complex tasks. In the software engineering domain, crowdsourced techniques have been adopted to facilitate various tasks, such as design, testing, debugging, development, and so on. Specifically, in crowdsourced testing, crowdsourced workers are given testing tasks to perform and submit their feedback in the form of test reports. One of the key advantages of crowdsourced testing is that it is capable of providing engineers software engineers with domain knowledge and feedback from a large number of real users. Based on diverse software and hardware settings of these users, engineers can bugs that are not caught by traditional quality assurance techniques. Such benefits are particularly ideal for mobile application testing, which needs rapid development-and-deployment iterations and support diverse execution environments. However, crowdsourced testing naturally generates an overwhelming number of crowdsourced test reports, and inspecting such a large number of reports becomes a time-consuming yet inevitable task. This dissertation presents a series of techniques, tools and experiments to assist in crowdsourced report processing. These techniques are designed for improving this task in multiple aspects: 1. prioritizing crowdsourced report to assist engineers in finding as many unique bugs as possible, and as quickly as possible; 2. grouping crowdsourced report to assist engineers in identifying the representative ones in a short time; 3. summarizing the duplicate reports to provide engineers with a concise and accurate understanding of a group of reports; In the first step, I present a text-analysis-based technique to prioritize test reports for manual inspection. This technique leverages two key strategies: (1) a diversity strategy to help developers inspect a wide variety of test reports and to avoid duplicates and wasted effort on falsely classified faulty behavior, and (2) a risk-assessment strategy to help developers identify test reports that may be more likely to be fault-revealing based on past observations.Together, these two strategies form our technique to prioritize test reports in crowdsourced testing. Moreover, in the mobile testing domain, test reports often consist of more screenshots and shorter descriptive text, and thus text-analysis-based techniques may be ineffective or inapplicable. The shortage and ambiguity of natural-language text information and the well-defined screenshots of activity views within mobile applications motivate me to propose a novel technique based on using image understanding for multi-objective test-report prioritization. This technique employs the Spatial Pyramid Matching (SPM) technique to measure the similarity of the screenshots, and apply the natural-language processing technique to measure the distance between the text of test reports. Next, I design and implement CTRAS: a novel approach to leveraging duplicates to enrich the content of bug descriptions and improve the efficiency of inspecting these reports. CTRAS is capable of automatically aggregating duplicates based on both textual information and screenshots, and further summarizes the duplicate test reports into a comprehensive and comprehensible report.I validate all of these techniques on industrial data by collaborating with several companies. The results show my techniques can improve both the efficiency and effectiveness of crowdsourced test report processing. Also, I suggest settings for different usage scenarios and discuss future research directions