Recommending Bug Assignment Approaches for Individual Bug Reports: An Empirical Investigation

Multiple approaches have been proposed to automatically recommend potential developers who can address bug reports. These approaches are typically designed to work for any bug report submitted to any software project. However, we conjecture that these approaches may not work equally well for all the reports in a project. We conducted an empirical study to validate this conjecture, using three bug assignment approaches applied on 2,249 bug reports from two open source systems. We found empirical evidence that validates our conjecture, which led us to explore the idea of identifying and applying the best-performing approach for each bug report to obtain more accurate developer recommendations. We conducted an additional study to assess the feasibility of this idea using machine learning. While we found a wide margin of accuracy improvement for this approach, it is far from achieving the maximum possible improvement and performs comparably to baseline approaches. We discuss potential reasons for these results and conjecture that the assignment approaches may not capture important information about the bug assignment process that developers perform in practice. The results warrant future research in understanding how developers assign bug reports and improving automated bug report assignmen

A Longitudinal Analysis of Bug Handling Across Eclipse Releases

International audienceLarge open source software projects, like Eclipse, follow a continuous software development process, with a regular release cycle. During each release, new bugs are reported, triaged and resolved. Previous studies have focused on various aspects of bug fixing, such as bug triaging, bug prediction, and bug process analysis. Most studies, however, do not distinguish between what happens before and after each scheduled release. We are also unaware of studies that compare bug fixing activities across different project releases. This paper presents an empirical analysis of the bug handling process of Eclipse over a 15-year period, considering 138K bug reports from Bugzilla, including 16 annual Eclipse releases and two quarterly releases in 2018. We compare the bug resolution rate, the fixing rate, the bug triaging time and the fixing time before and after each release date, and we study the possible impact of "release pressure". Among others, our results reveal that Eclipse bug handling activity is improving over time, with an important decrease in the number of reported bugs before releases, an increase in the bug fixing rate and an increasingly balanced bug handling workload before and after releases. The recent transition from an annual to a quarterly release cycle continued to improve the bug handling process

Representational Learning Approach for Predicting Developer Expertise Using Eye Movements

The thesis analyzes an existing eye-tracking dataset collected while software developers were solving bug fixing tasks in an open-source system. The analysis is performed using a representational learning approach namely, Multi-layer Perceptron (MLP). The novel aspect of the analysis is the introduction of a new feature engineering method based on the eye-tracking data. This is then used to predict developer expertise on the data. The dataset used in this thesis is inherently more complex because it is collected in a very dynamic environment i.e., the Eclipse IDE using an eye-tracking plugin, iTrace. Previous work in this area only worked on short code snippets that do not represent how developers usually program in a realistic setting. A comparative analysis between representational learning and non-representational learning (Support Vector Machine, Naive Bayes, Decision Tree, and Random Forest) is also presented. The results are obtained from an extensive set of experiments (with an 80/20 training and testing split) which show that representational learning (MLP) works well on our dataset reporting an average higher accuracy of 30% more for all tasks. Furthermore, a state-of-the-art method for feature engineering is proposed to extract features from the eye-tracking data. The average accuracy on all the tasks is 93.4% with a recall of 78.8% and an F1 score of 81.6%. We discuss the implications of these results on the future of automated prediction of developer expertise. Adviser: Bonita Shari

Which packages would be affected by this bug report?

NSFC Progra

Assisting Software Developers With License Compliance

Open source licensing determines how open source systems are reused, distributed, and modified from a legal perspective. While it facilitates rapid development, it can present difficulty for developers in understanding due to the legal language of these licenses. Because of misunderstandings, systems can incorporate licensed code in a way that violates the terms of the license. Such incompatibilities between licensing can result in the inability to reuse a particular library without either relicensing the system or redesigning the architecture of the system. Prior efforts have predominantly focused on license identification or understanding the underlying phenomena without reasoning about compatibility in a broad scale. The work in this dissertation first investigates the rationale of developers and identifies the areas that developers struggle with respect to free/open source software licensing. First, we investigate the diffusion of licenses and the prevalence of license changes in a large scale empirical study of 16,221 Java systems. We observed a clear lack of traceability and a lack of standardized licensing that led to difficulties and confusion for developers trying to reuse source code. We further investigated the difficulty by surveying the developers of the systems with license changes to understand why they first adopted a license and then changed licenses. Additionally, we performed an analysis on issue trackers and legal mailing lists to extract licensing bugs. From these works, we identified key areas in which developers struggled and needed support. While developers need support to identify license incompatibilities and understand both the cause and implications of the incompatibilities, we observed that state-of-the-art license identification tools did not identify license exceptions. Since these exceptions directly modify the license terms (either the permissions granted by the license or the restrictions imposed by the license), we proposed an approach to complement current license identification techniques in order to classify license exceptions. The approach relies on supervised machine learners to classify the licensing text to identify the particular license exceptions or the lack of a license exception. Subsequently, we built an infrastructure to assist developers with evaluating license compliance warnings for their system. The infrastructure evaluates compliance across the dependency tree of a system to ensure it is compliant with all of the licenses of the dependencies. When an incompatibility is present, it notes the specific library/libraries and the conflicting license(s) so that the developers can investigate these compliance warnings, which would prevent distribution of their software, in their system. We conduct a study on 121,094 open source projects spanning 6 programming languages, and we demonstrate that the infrastructure is able to identify license incompatibilities between these projects and their dependencies

30 Years of Software Refactoring Research:A Systematic Literature Review

Due to the growing complexity of software systems, there has been a dramatic increase and industry demand for tools and techniques on software refactoring in the last ten years, defined traditionally as a set of program transformations intended to improve the system design while preserving the behavior. Refactoring studies are expanded beyond code-level restructuring to be applied at different levels (architecture, model, requirements, etc.), adopted in many domains beyond the object-oriented paradigm (cloud computing, mobile, web, etc.), used in industrial settings and considered objectives beyond improving the design to include other non-functional requirements (e.g., improve performance, security, etc.). Thus, challenges to be addressed by refactoring work are, nowadays, beyond code transformation to include, but not limited to, scheduling the opportune time to carry refactoring, recommendations of specific refactoring activities, detection of refactoring opportunities, and testing the correctness of applied refactorings. Therefore, the refactoring research efforts are fragmented over several research communities, various domains, and objectives. To structure the field and existing research results, this paper provides a systematic literature review and analyzes the results of 3183 research papers on refactoring covering the last three decades to offer the most scalable and comprehensive literature review of existing refactoring research studies. Based on this survey, we created a taxonomy to classify the existing research, identified research trends, and highlighted gaps in the literature and avenues for further research.Comment: 23 page