Search for: All records

Information Retrieval (IR) methods have been recently employed to provide automatic support for bug localization tasks. However, for an IR-based bug localization tool to be useful, it has to achieve adequate retrieval accuracy. Lower precision and recall can leave developers with large amounts of incorrect information to wade through. To address this issue, in this paper, we systematically investigate the impact of combining various IR methods on the retrieval accuracy of bug localization engines. The main assumption is that different IR methods, targeting different dimensions of similarity between artifacts, can be used to enhance the confidence in each others' results. Five benchmark systems from different application domains are used to conduct our analysis. The results show that a) near-optimal global configurations can be determined for different combinations of IR methods, b) optimized IR-hybrids can significantly outperform individual methods as well as other unoptimized methods, and c) hybrid methods achieve their best performance when utilizing information-theoretic IR methods. Our findings can be used to enhance the practicality of IR-based bug localization tools and minimize the cognitive overload developers often face when locating bugs. 

Background and Context: GitHub has been recently used in Software Engineering (SE) classes to facilitate collaboration in student team projects as well as help teachers to evaluate the contributions of their students more objectively. Objective: We explore the benefits and drawbacks of using GitHub as a means for team collaboration and performance evaluation in large SE classes. Method: Our research method takes the form of a case study conducted in a senior level SE class with 91 students. Our study also includes entry and exit surveys, an exit interview, and a qualitative analysis of students’ commit behavior. Findings: Different teams adapt GitHub to their workflow differently. Furthermore, despite the steep learning curve, using GitHub should not affect the quality of students’ submissions. However, using GitHub metrics as a proxy for evaluating team performance can be risky. Implications: We provide several recommendations for integrating Web-based configuration management tools in SE classes. 

In this paper, we seek to advance the state-of-the-art in code evolution analysis research and practice by statistically analyzing, interpreting, and formally describing the evolution of code lexicon in Open Source Software (OSS). The underlying hypothesis is that, similar to natural language, code lexicon falls under the remit of evolutionary principles. Therefore, adapting theories and statistical models of natural language evolution to code is expected to provide unique insights into software evolution. Our analysis in this paper is conducted using 2,000 OSS systems sampled from a broad range of application domains. Our results show that a) OSS projects exhibit a significant shift in their linguistic identity over time, b) different syntactic structures of code lexicon evolve differently, c) different factors of OSS development and different maintenance activities impact code lexicon differently. These insights lay out a preliminary foundation for modeling the linguistic history of OSS projects. In the long run, this foundation will be utilized to provide support for basic software maintenance and program comprehension activities, and gain new theoretical insights into the complex interplay between linguistic change and various system and human aspects of OSS development.