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

OpenReq Issue Link Map : A Tool to Visualize Issue Links in Jira

Peer reviewe

Effective Natural Language Interfaces for Data Visualization Tools

How many Covid cases and deaths are there in my hometown? How much money was invested into renewable energy projects across states in the last 5 years? How large was the biggest investment in solar energy projects in the previous year? These questions and others are of interest to users and can often be answered by data visualization tools (e.g., COVID-19 dashboards) provided by governmental organizations or other institutions. However, while users in organizations or private life with limited expertise with data visualization tools (hereafter referred to as end users) are also interested in these topics, they do not necessarily have knowledge of how to use these data visualization tools effectively to answer these questions. This challenge is highlighted by previous research that provided evidence suggesting that while business analysts and other experts can effectively use these data visualization tools, end users with limited expertise with data visualization tools are still impeded in their interactions. One approach to tackle this problem is natural language interfaces (NLIs) that provide end users with a more intuitive way of interacting with these data visualization tools. End users would be enabled to interact with the data visualization tool both by utilizing the graphical user interface (GUI) elements and by just typing or speaking a natural language (NL) input to the data visualization tool. While NLIs for data visualization tools have been regarded as a promising approach to improving the interaction, two design challenges still remain. First, existing NLIs for data visualization tools still target users who are familiar with the technology, such as business analysts. Consequently, the unique design required by end users that address their specific characteristics and that would enable the effective use of data visualization tools by them is not included in existing NLIs for data visualization tools. Second, developers of NLIs for data visualization tools are not able to foresee all NL inputs and tasks that end users want to perform with these NLIs for data visualization tools. Consequently, errors still occur in current NLIs for data visualization tools. End users need to be therefore enabled to continuously improve and personalize the NLI themselves by addressing these errors. However, only limited work exists that focus on enabling end users in teaching NLIs for data visualization tools how to correctly respond to new NL inputs. This thesis addresses these design challenges and provides insights into the related research questions. Furthermore, this thesis contributes prescriptive knowledge on how to design effective NLIs for data visualization tools. Specifically, this thesis provides insights into how data visualization tools can be extended through NLIs to improve their effective use by end users and how to enable end users to effectively teach NLIs how to respond to new NL inputs. Furthermore, this thesis provides high-level guidance that developers and providers of data visualization tools can utilize as a blueprint for developing data visualization tools with NLIs for end users and outlines future research opportunities that are of interest in supporting end users to effectively use data visualization tools

It Takes a Village: Open Source Software Sustainability

This Guidebook is designed to serve as a practical reference source to help open source software programs serving cultural and scientific heritage organizations plan for long-term sustainability, ensuring that commitment and resources will be available at levels sufficient for the software to remain viable and effective as long as it is needed. One of the most significant themes of this Guidebook is that sustainability is not a linear process, with set beginning and end points. Program sustainability shifts and evolves over time across a number of phases and facets. The phases speak to where a program is in its lifecycle: getting started, growing, or stable but not static. The facets describe the different components of sustainability, each of which is critical to overall program health, but may have different timelines, goals, and resource needs. The facets deemed most critical by the Guidebook’s authors and contributors are: Governance, Technology, Resources (Financial and Human), and Community Engagement. Sections of the Guidebook will: define the phases and facets of sustainability; identify goals, characteristics, and common roadblocks for each phase in each facet; provide guidance for moving an OSS program to the next phase in a given facet, with the understanding that the same program may be in different phases along different facets of sustainability; and highlight case studies and additional resources to help a program’s research and decision-making process. The Guidebook is intended for a broad audience. While certain paths may be of more interest than others, we would recommend reading through each of the facets before returning to the one that aligns most closely with a specific role, e.g., governance for a program manager, technology for a technical lead, engagement for a community manager, or resources for an administrator. The worksheet in Appendix A can help identify the specific phase a program is in along each facet. The open source landscape is wide and varied. Bringing open source programs serving cultural and scientific heritage together under one shared umbrella can provide us all with the power to better advocate for our needs, develop shared sustainability strategies, and provide our communities with the information needed to assess and contribute to the sustainability of the programs they depend on

Strategies to Reduce Employee Turnover in a National Grocery Chain

Retaining qualified employees is a problem for many organizations, which costs companies both monetary resources and hours of productivity. A contributing factor to the problem of employee retention is the lack of trained managers who are equipped to foster and increase employee job satisfaction. The purpose of this single case study, using a transformational leadership framework, was to explore managerial strategies to reduce turnover at 1 store in a national grocery store in the Midwestern United States. Methodological triangulation was achieved through the semistructured interviews of 5 managers, as well as a review of company training documents, and a review of the company\u27s website. Prior to the interviews, 1 manager was interviewed as a pilot study (for validation of the interview questions). Three main themes emerged from coding the transcribed data: implementing effective management practices and an approachable leadership style, increasing and maintaining job satisfaction, and planning for future employee attraction and retention. In addition, several subthemes emerged in each of these broader categories of strategies. According to study findings, transformational leadership style was a successful strategy in employee retention in some instances. The implications for positive social change include the potential to reduce turnover and unemployment, as well as for organizations to create a supportive workplace for their staff

How Early Participation Determines Long-Term Sustained Activity in GitHub Projects?

Although the open source model bears many advantages in software development, open source projects are always hard to sustain. Previous research on open source sustainability mainly focuses on projects that have already reached a certain level of maturity (e.g., with communities, releases, and downstream projects). However, limited attention is paid to the development of (sustainable) open source projects in their infancy, and we believe an understanding of early sustainability determinants is crucial for project initiators, incubators, newcomers, and users. In this paper, we aim to explore the relationship between early participation factors and long-term project sustainability. We leverage a novel methodology combining the Blumberg model of performance and machine learning to predict the sustainability of 290,255 GitHub projects. Specificially, we train an XGBoost model based on early participation (first three months of activity) in 290,255 GitHub projects and we interpret the model using LIME. We quantitatively show that early participants have a positive effect on project's future sustained activity if they have prior experience in OSS project incubation and demonstrate concentrated focus and steady commitment. Participation from non-code contributors and detailed contribution documentation also promote project's sustained activity. Compared with individual projects, building a community that consists of more experienced core developers and more active peripheral developers is important for organizational projects. This study provides unique insights into the incubation and recognition of sustainable open source projects, and our interpretable prediction approach can also offer guidance to open source project initiators and newcomers.Comment: The 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE 2023

App Review Driven Collaborative Bug Finding

Software development teams generally welcome any effort to expose bugs in their code base. In this work, we build on the hypothesis that mobile apps from the same category (e.g., two web browser apps) may be affected by similar bugs in their evolution process. It is therefore possible to transfer the experience of one historical app to quickly find bugs in its new counterparts. This has been referred to as collaborative bug finding in the literature. Our novelty is that we guide the bug finding process by considering that existing bugs have been hinted within app reviews. Concretely, we design the BugRMSys approach to recommend bug reports for a target app by matching historical bug reports from apps in the same category with user app reviews of the target app. We experimentally show that this approach enables us to quickly expose and report dozens of bugs for targeted apps such as Brave (web browser app). BugRMSys's implementation relies on DistilBERT to produce natural language text embeddings. Our pipeline considers similarities between bug reports and app reviews to identify relevant bugs. We then focus on the app review as well as potential reproduction steps in the historical bug report (from a same-category app) to reproduce the bugs. Overall, after applying BugRMSys to six popular apps, we were able to identify, reproduce and report 20 new bugs: among these, 9 reports have been already triaged, 6 were confirmed, and 4 have been fixed by official development teams, respectively

Testing Matching and Mirroring With Homophily in Onboarding Leadership Socialization

This study was designed to test the relationship between matching and mirroring (MM) and homophilous perceptions (PHM) in leadership socialization. Elevated PHM levels were hypothesized to affect workplace acceptance levels. The need for testing leadership socialization skills was magnified with the current demographic shift known as the leadership succession crisis, creating problems with onboarding strategies. The theoretical foundations of the study were based on the social identity theory, the social presence theory, the leader-member exchange theory, and the similarity-attraction paradigm. The study conducted at Workforce Solutions North Texas in Wichita Falls, Texas was sampled based on the calculated strength of the effect in a pilot study. Test group participants engaged in MM enhanced social conversation with a coached candidate and control group participants conversed with an uncoached participant from the general population engaging in normal conversation. MM processes were differentiated from natural synchronic tendencies using specialized software and Kinect-® sensors. A contrasted group, quasi-experiment was examined with an analysis of covariance. No statistically significant difference was found between groups on PHM levels, correcting for age, gender, ethnicity, height, glasses, hobbies, and professions. However, PHM and coworker acceptance were statistically significant but with no difference between groups. Further research is needed to test PHM as a metric for rapport in socialization strategies. Nevertheless, the homophily lens rather than the rapport lens can help organizational development and human resource professionals quantify and develop more effective socialization strategies aimed at solving problems associated with the leadership succession crisis

How ChatGPT is changing digital marketing activities: The Aleluya’s Case.

openIn recent years, several terms related to artificial intelligence have come to the forefront of everyday conversations, leaving us both delighted and confused about its scope. Initially, this paper shows that AI is not something that was born sporadically in recent years, on the contrary it is something that has been worked on for more than half a decade and has had major milestones in its history. One of those milestones is ChatGPT, an AI-powered chatbot capable of generating human-like text responses in a conversational manner. This new technology holds great promise in almost every industry, and this paper focuses on evidencing the different scenarios in which ChatGPT can transform companies' marketing activities, making them more efficient, innovative and user-friendly. But it also considers its implications and challenges. Finally, a case study is shown in the Colombian startup Aleluya, in which an experiment was carried out implementing ChatGPT in their marketing activities in order to generate an appropriation of this new technology and to see in practice its advantages and disadvantages