With the advent of social media, developers are increasingly using it in their software development activities. Twitter is one of the popular social mediums used by developers. A recent study by Singer et al. found that software developers use Twitter to “keep up with the fast-paced development landscape.” Unfortunately, due to the general-purpose nature of Twitter, it’s challenging for developers to use Twitter for their development activities. Our survey with 36 developers who use Twitter in their development activities highlights that developers are interested in following specialized software gurus who share relevant technical tweets. To help developers perform this task, in this work we propose a recommendation system to identify specialized software gurus. Our approach first extracts different kinds of features that characterize a Twitter user and then employs a two-stage classification approach to generate a discriminative model, which can differentiate specialized software gurus in a particular domain from other Twitter users that generate domain-related tweets (aka domain-related Twitter users). We have investigated the effectiveness of our approach in finding specialized software gurus for four different domains (JavaScript, Android, Python, and Linux) on a dataset of 86,824 Twitter users who generate 5,517,878 tweets over 1 month. Our approach can differentiate specialized software experts from other domain-related Twitter users with an F-Measure of up to 0.820. Compared with existing Twitter domain expert recommendation approaches, our proposed approach can outperform their F-Measure by at least 7.63%.

Projects receive a large number of bug reports, and resolving these reports take considerable time and human resources. To aid developers in the resolution of bug reports, various automated techniques have been proposed to identify and recommend developers to address newly reported bugs. Two families of bug assignee recommendation techniques include those that recommend developers who have fixed similar bugs before (a.k.a. activity-based techniques) and those recommend suitable developers based on the location of the bug (a.k.a. location-based techniques). Previously, each of these techniques has been investigated separately. In this work, we propose a unified model that combines information from both developers' previous activities and suspicious program locations associated with a bug report in the form of similarity features. We have evaluated our proposed approach on more than 11,000 bug reports from Eclipse JDT, Eclipse SWT and ArgoUML projects. Our experiments show that our unified model can outperform a location-based baseline by Anvik et al. and an activity-based baseline by Shokripour et al. In terms of correct recommendations at top-1 position, our unified model outperforms the activity-based baseline 50.0%-100.0%, and the location-based baseline by 11.1%-27.0%.

Nowadays, most software use multiple programming languages to implement certain functionalities based on the strengths and weaknesses of different languages. Researchers in the past have studied the impact of independent programming languages on software quality, however, there has been little or no research on the impact of multiple languages on the quality of software. Does the use of multiple languages cause more bugs? Are certain languages when used with other languages make software more bug prone? What are the relationships between multi-language usage and various bug categories? In this study, we perform a large scale empirical investigation to provide some answers to these questions. We gather a large dataset consisting of popular projects from GitHub (628 projects, 85 million SLOC, 134 thousand authors, 3 million commits, in 17 languages) to understand the impact of using multiple languages on software quality. We build multiple regression models to study the effects of using different languages on the number of bug fixing commits while controlling for factors such as project age, project size, team size, and the number of commits. Our results show that in general implementing a project with more languages has a significant effect on project quality, as it increases defect proneness. Moreover, we find specific languages that are statistically significantly more defect prone when they are used in a multi-language setting. These include popular languages like C++, Objective-C, and Java. Furthermore, we note that the use of more languages significantly increases bug proneness across all bug categories. The effect is strongest for memory, concurrency, and algorithm bugs.