Features that Predict the Acceptability of Java and JavaScript Answers on Stack Overflow

Abstract

Context: It is not uncommon for a new team member to join an existing Agile software development team, even after development has started. This new team member faces a number of challenges before they are integrated into the team and can contribute productively to team progress. Ideally, each newcomer should be supported in this transition through an effective team onboarding program, although prior evidence suggests that this is challenging for many organisations. Objective: We seek to understand how Agile teams address the challenge of team onboarding in order to inform future onboarding design. Method: We conducted an interview survey of eleven participants from eight organisations to investigate what onboarding activities are common across Agile software development teams. We also identify common goals of onboarding from a synthesis of literature. A repertory grid instrument is used to map the contributions of onboarding techniques to onboarding goals. Results: Our study reveals that a broad range of team onboarding techniques, both formal and informal, are used in practice. It also shows that particular techniques that have high contributions to a given goal or set of goals. Conclusions: In presenting a set of onboarding goals to consider and an evidence-based mechanism for selecting techniques to achieve the desired goals it is expected that this study will contribute to betterinformed onboarding design and planning. An increase in practitioner awareness of the options for supporting new team members is also an expected outcome.

Learning Features that Predict Developer Responses for iOS App Store Reviews

“…(1) Prior work -Rather than starting from scratch, we chose to leverage the wealth of knowledge from prior work to focus on features that have been shown to associate with response decisions in other areas of study (e.g., in Q&A forums [20] and in email communications [35]). (2) The collection method -Due to the unstructured and informal nature of user reviews, some features are not trivial to extract.…”

“…For this, Spearman's correlation is used to identify correlated features, due to its ability to model complex monotonic relationships among features rather than just linear as modeled by Pearson correlation method. By following Omondiagbe et al [20], feature pairs with absolute correlation value of 0.7 or higher are marked as highly correlated. Among these identified pairs, the ones with a lower mutual information [15] are subsequently removed.…”

Trend Analysis of Large Language Models through a Developer Community: A Focus on Stack Overflow

“…Investigating this aspect is vital, as understanding the realworld application and discourse around LLMs can connect theoretical advancements with practical implementations [25,26]. Previous studies have delved into diverse topics, such as predicting the acceptability of answers in specific programming languages [27], understanding refactoring discussions [19], and exploring the use of technologies like Apache Spark [28]. Additionally, research to comprehend discussions around deep learning frameworks has been undertaken, as evidenced by the mining and comparing of discussions on platforms like Stack Overflow and GitHub [20].…”