Personalized First Issue Recommender for Newcomers in Open Source Projects

Many open source projects provide good first issues (GFIs) to attract and retain newcomers. Although several automated GFI recommenders have been proposed, existing recommenders are limited to recommending generic GFIs without considering differences between individual newcomers. However, we observe mismatches between generic GFIs and the diverse background of newcomers, resulting in failed attempts, discouraged onboarding, and delayed issue resolution. To address this problem, we assume that personalized first issues (PFIs) for newcomers could help reduce the mismatches. To justify the assumption, we empirically analyze 37 newcomers and their first issues resolved across multiple projects. We find that the first issues resolved by the same newcomer share similarities in task type, programming language, and project domain. These findings underscore the need for a PFI recommender to improve over state-of-the-art approaches. For that purpose, we identify features that influence newcomers' personalized selection of first issues by analyzing the relationship between possible features of the newcomers and the characteristics of the newcomers' chosen first issues. We find that the expertise preference, OSS experience, activeness, and sentiment of newcomers drive their personalized choice of the first issues. Based on these findings, we propose a Personalized First Issue Recommender (PFIRec), which employs LamdaMART to rank candidate issues for a given newcomer by leveraging the identified influential features. We evaluate PFIRec using a dataset of 68,858 issues from 100 GitHub projects. The evaluation results show that PFIRec outperforms existing first issue recommenders, potentially doubling the probability that the top recommended issue is suitable for a specific newcomer and reducing one-third of a newcomer's unsuccessful attempts to identify suitable first issues, in the median.Comment: The 38th IEEE/ACM International Conference on Automated Software Engineering (ASE 2023

SUPPORTING DEVELOPER-ONBOARDING WITH ENHANCED RESOURCE FINDING AND VISUAL EXPLORATION

Understanding the basic structure of a code base and a development team are essential to get new developers up to speed in a software development project. Developers do so through the process of early experimentation with code and the creation of mental models of technical and social structures in a project. However, getting up-to-speed in a new project can be challenging due to difficulties in: finding the right place to begin explorations, expanding the focus to determine relevant resources for tasks, and identifying dependencies across project elements to gain a high-level overview of project structures. In this thesis, I first identified six challenges that developers face during the process of developer onboarding from recent research studies and informal interviews with developers. To address these challenges, I implemented automated tool support with enhanced resource finding and visual exploration. Specifically, I proposed six functional requirements for supporting developers onboarding. I then extended the project tool Tesseract to support these functionalities to help novice developers and relevant resources (files, developers, bugs, etc.) and understand project structures when joining a new project. To understand how the onboarding functionalities work in supporting developers\u27 onboarding process, I conducted a user study with typical onboarding tasks requiring early experimentation and internalizing project structures. The results indicated that enhanced search features, the ability to explore semantic relationships across repositories, and network-centric visualizations of project structures were very effective in supporting onboarding

Examining the Impact of Algorithm Awareness on Wikidata's Recommender System Recoin

The global infrastructure of the Web, designed as an open and transparent system, has a significant impact on our society. However, algorithmic systems of corporate entities that neglect those principles increasingly populated the Web. Typical representatives of these algorithmic systems are recommender systems that influence our society both on a scale of global politics and during mundane shopping decisions. Recently, such recommender systems have come under critique for how they may strengthen existing or even generate new kinds of biases. To this end, designers and engineers are increasingly urged to make the functioning and purpose of recommender systems more transparent. Our research relates to the discourse of algorithm awareness, that reconsiders the role of algorithm visibility in interface design. We conducted online experiments with 105 participants using MTurk for the recommender system Recoin, a gadget for Wikidata. In these experiments, we presented users with one of a set of three different designs of Recoin's user interface, each of them exhibiting a varying degree of explainability and interactivity. Our findings include a positive correlation between comprehension of and trust in an algorithmic system in our interactive redesign. However, our results are not conclusive yet, and suggest that the measures of comprehension, fairness, accuracy and trust are not yet exhaustive for the empirical study of algorithm awareness. Our qualitative insights provide a first indication for further measures. Our study participants, for example, were less concerned with the details of understanding an algorithmic calculation than with who or what is judging the result of the algorithm.Comment: 10 pages, 7 figure

Designing and presenting digital nudges on mobile phones Building an app based on system requirements and usability heuristics

The environment is progressively affected by global warming and pollution, whereas fossil fuel transportation is one of the major causes. This thesis describes a system that aims to support users in choosing environmentally friendly transportation alternatives. The system uses digital nudging to motivate behavioral change in a non-intrusive manner. This project focuses on the presentation of nudging in a mobile environment. Mobile applications reside in a complex environment with many constraints and limitations. The applications also communicate and influence the end users based on architectural and front-end components. Such applications should thus follow strict guidelines to ensure a robust, extendable, and reusable foundation. Furthermore, the applications should utilize various techniques based on psychological effects and user experience principles to stay competitive in the current market. This project presents a selection of psychological requirements designed for nudging. Additionally, the project creates a novel set of usability heuristics designed for nudging. The project implements an Android app based on the requirements and heuristics. The app lays the foundation for future extensions of front-end designs and nudging

Examining the Impact of Algorithm Awareness on {W}ikidata's Recommender System Recoin

The global infrastructure of the Web, designed as an open and transparent system, has a significant impact on our society. However, algorithmic systems of corporate entities that neglect those principles increasingly populated the Web. Typical representatives of these algorithmic systems are recommender systems that influence our society both on a scale of global politics and during mundane shopping decisions. Recently, such recommender systems have come under critique for how they may strengthen existing or even generate new kinds of biases. To this end, designers and engineers are increasingly urged to make the functioning and purpose of recommender systems more transparent. Our research relates to the discourse of algorithm awareness, that reconsiders the role of algorithm visibility in interface design. We conducted online experiments with 105 participants using MTurk for the recommender system Recoin, a gadget for Wikidata. In these experiments, we presented users with one of a set of three different designs of Recoin's user interface, each of them exhibiting a varying degree of explainability and interactivity. Our findings include a positive correlation between comprehension of and trust in an algorithmic system in our interactive redesign. However, our results are not conclusive yet, and suggest that the measures of comprehension, fairness, accuracy and trust are not yet exhaustive for the empirical study of algorithm awareness. Our qualitative insights provide a first indication for further measures. Our study participants, for example, were less concerned with the details of understanding an algorithmic calculation than with who or what is judging the result of the algorithm

Developers' Visuo-spatial Mental Model and Program Comprehension

Previous works from research and industry have proposed a spatial representation of code in a canvas, arguing that a navigational code space confers developers the freedom to organise elements according to their understanding. By allowing developers to translate logical relatedness into spatial proximity, this code representation could aid in code navigation and comprehension. However, the association between developers' code comprehension and their visuo-spatial mental model of the code is not yet well understood. This mental model is affected on the one hand by the spatial code representation and on the other by the visuo-spatial working memory of developers. We address this knowledge gap by conducting an online experiment with 20 developers following a between-subject design. The control group used a conventional tab-based code visualization, while the experimental group used a code canvas to complete three code comprehension tasks. Furthermore, we measure the participants' visuo-spatial working memory using a Corsi Block test at the end of the tasks. Our results suggest that, overall, neither the spatial representation of code nor the visuo-spatial working memory of developers has a significant impact on comprehension performance. However, we identified significant differences in the time dedicated to different comprehension activities such as navigation, annotation, and UI interactions.Comment: To appear in 2023 International Conference on Software Engineering (ICSE 2023). Authors' version of the wor