Using Crowd-Based Software Repositories to Better Understand Developer-User Interactions

Software development is a complex process. To serve the final software product to the end user, developers need to rely on a variety of software artifacts throughout the development process. The term software repository used to denote only containers of source code such as version control systems; more recent usage has generalized the concept to include a plethora of software development artifact kinds and their related meta-data. Broadly speaking, software repositories include version control systems, technical documentation, issue trackers, question and answer sites, distribution information, etc. The software repositories can be based on a specific project (e.g., bug tracker for Firefox), or be crowd-sourced (e.g., questions and answers on technical Q&A websites). Crowd-based software artifacts are created as by-products of developer-user interactions which are sometimes referred to as communication channels. In this thesis, we investigate three distinct crowd-based software repositories that follow different models of developer-user interactions. We believe through a better understanding of the crowd-based software repositories, we can identify challenges in software development and provide insights to improve the software development process. In our first study, we investigate Stack Overflow. It is the largest collection of programming related questions and answers. On Stack Overflow, developers interact with other developers to create crowd-sourced knowledge in the form of questions and answers. The results of the interactions (i.e., the question threads) become valuable information to the entire developer community. Prior research on Stack Overflow tacitly assume that questions receives answers directly on the platform and no need of interaction is required during the process. Meanwhile, the platform allows attaching comments to questions which forms discussions of the question. Our study found that question discussions occur for 59.2% of questions on Stack Overflow. For discussed and solved questions on Stack Overflow, 80.6% of the questions have the discussion begin before the accepted answer is submitted. The results of our study show the importance and nuances of interactions in technical Q&A. We then study dotfiles, a set of publicly shared user-specific configuration files for software tools. There is a culture of sharing dotfiles within the developer community, where the idea is to learn from other developers’ dotfiles and share your variants. The interaction of dotfiles sharing can be viewed as developers sources information from other developers, adapt the information to their own needs, and share their adaptations back to the community. Our study on dotfiles suggests that is a common practice among developers to share dotfiles where 25.8% of the most stared users on GitHub have a dotfiles repository. We provide a taxonomy of the commonly tracked dotfiles and a qualitative study on the commits in dotfiles repositories. We also leveraged the state-of-the-art time-series clustering technique (K-shape) to identify code churn pattern for dotfile edits. This study is the first step towards understanding the practices of maintaining and sharing dotfiles. Finally, we study app stores, the platforms that distribute software products and contain many non-technical attributes (e.g., ratings and reviews) of software products. Three major stakeholders interacts with each other in app stores: the app store owner who governs the operation of the app store; developers who publish applications on the app store; and users who browse and download applications in the app store. App stores often provide means of interaction between all three actors (e.g., app reviews, store policy) and sometimes interactions with in the same actor (e.g., developer forum). We surveyed existing app stores to extract key features from app store operation. We then labeled a representative set of app store collected by web queries. K-means is applied to the labeled app stores to detect natural groupings of app stores. We observed a diverse set of app stores through the process. Instead of a single model that describes all app stores, fundamentally, our observations show that app stores operates differently. This study provide insights in understanding how app stores can affect software development. In summary, we investigated software repositories containing software artifacts created from different developer-user interactions. These software repositories are essential for software development in providing referencing information (i.e., Stack Overflow), improving development productivity (i.e., dotfiles), and help distributing the software products to end users (i.e., app stores)

Causal impact analysis for app releases in google play

App developers would like to understand the impact of their own and their competitors' software releases. To address this we introduce Causal Impact Release Analysis for app stores, and our tool, CIRA, that implements this analysis. We mined 38,858 popular Google Play apps, over a period of 12 months. For these apps, we identified 26,339 releases for which there was adequate prior and posterior time series data to facilitate causal impact analysis. We found that 33% of these releases caused a statistically significant change in user ratings. We use our approach to reveal important characteristics that distinguish causal significance in Google Play. To explore the actionability of causal impact analysis, we elicited the opinions of app developers: 56 companies responded, 78% concurred with the causal assessment, of which 33% claimed that their company would consider changing its app release strategy as a result of our findings

Effects of Personality Traits and Emotional Factors in Pull Request Acceptance.

Social interactions in the form of discussion are an indispensable part of collaborative software development. The discussions are essential for developers to share their views and to form a strong relationship with other teammates. These discussions invoke both positive and negative emotions such as joy, love, aggression, and disgust. Additionally, developers also exhibit hidden behaviours that dictate their personality. Some developers can be supportive and open to new ideas, whereas others can be conservative. Past research has shown that the personality of the developers has a significant role in determining the success of the task they collaboratively perform. Additionally, previous research has also shown that in online collaborative environments, the developers use signals from comments such as rudeness to determine if they are compatible to work together. Most of these studies use traditional small-scale surveys for their experiments. The transparent nature of online collaborative environments makes it easier to conduct empirical experiments by mining pull request comments. In this thesis, first, we investigate the effect of different personality traits on pull request acceptance. The results of this experiment will provide us with a valuable understanding of the personality traits of developers and help us develop tools to assist developers. We follow it with a second experiment to understand the influence of different emotional factors on pull request decisions. The emotion expressed by a developer on their teammates can be influenced by social statuses, such as the number of followers. Moreover, the teammate's team status, such as team member or outside contributor too, can influence the emotional effect. To understand moderation, we investigate different interaction effects. We start the experiment by replicating Tsay et al.'s work that examined the influence of social factors (e.g., `social distance') and technical factors (e.g., test file inclusion) for evaluating contributions. We extend their work by augmenting it with personality traits of developers and examining the influence of on the pull request evaluation process in GitHub. In particular, we extract the `Big Five' personality traits (Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism) of developers from their online digital footprints, such as pull request comments. We analyze the personality traits of 16,935 active developers from 1,860 projects and compare their relative importance to other non-personality factors from past research, in the pull request evaluation process. We find that pull requests from authors (requesters) who are more open and conscientious, but less extroverted, have a higher chance of approval. Furthermore, pull requests that are closed by developers (closers) who are more conscientious, extroverted, and neurotic, have a higher likelihood of acceptance. The larger the difference in personality traits between the requester and the closer, the more positive effect it has on pull request acceptance. Although the effect of personality traits is significant and comparable to technical factors, we find that social factors are still more influential when it comes to the effect in the likelihood of pull request acceptance. We perform a second experiment to analyze the effect of emotions on pull request decisions. To predict emotions in the comments, we develop a generalised, software engineering specific language model that outperforms previous machine learning algorithms on four different standard datasets. We find that the percentage of positive comments from both requester and closer has a positive association with pull request acceptance, whereas the percentage of negative comments has a negative association. Also, the polarity of the emotion associated with the first comment of both requester and closer had a positive association with pull request acceptance, i.e., more positive the emotion, the higher the likelihood of acceptance. Finally, we find that social factors moderate the effects of emotions

A survey of app store analysis for software engineering

App Store Analysis studies information about applications obtained from app stores. App stores provide a wealth of information derived from users that would not exist had the applications been distributed via previous software deployment methods. App Store Analysis combines this non-technical information with technical information to learn trends and behaviours within these forms of software repositories. Findings from App Store Analysis have a direct and actionable impact on the software teams that develop software for app stores, and have led to techniques for requirements engineering, release planning, software design, security and testing. This survey describes and compares the areas of research that have been explored thus far, drawing out common aspects, trends and directions future research should take to address open problems and challenges

Customer Rating Reactions Can Be Predicted Purely Using App Features

In this paper we provide empirical evidence that the rating that an app attracts can be accurately predicted from the features it offers. Our results, based on an analysis of 11,537 apps from the Samsung Android and BlackBerry World app stores, indicate that the rating of 89% of these apps can be predicted with 100% accuracy. Our prediction model is built by using feature and rating information from the existing apps offered in the App Store and it yields highly accurate rating predictions, using only a few (11-12) existing apps for case-based prediction. These findings may have important implications for require- ments engineering in app stores: They indicate that app devel- opers may be able to obtain (very accurate) assessments of the customer reaction to their proposed feature sets (requirements), thereby providing new opportunities to support the requirements elicitation process for app developers