Build Issue Resolution from the Perspective of Non-Contributors

GitHub enables developers to expediently contribute their comments on multiple issues and switch their discussion between issues, i.e., multi-discussing. Discussing multiple issues simultaneously is able to enhance work efficiency. However, multi-discussing also relies on developers’ rationally allocating their focus, which may result in the different influence on the resolution of issues. Therefore, investigating how multi-discussing affects the issue resolution is a meaningful research question that can help developers understand the benefits and limitations of multi-discussing. Using quantitative and qualitative methods, this paper proposes a groundbreaking study of the impact of multi-discussing on issue resolution in GitHub. First, we collect and analyze data from 624 GitHub projects to explore how multi-discussing affects the overall issue resolution of the project. Further, we investigate how multi-discussing affects the resolution of a single issue. We find that multi-discussing is a common behavior in GitHub. Also, multi-discussing is connected to a shorter average issue resolution latency of the project. However, during a single issue resolution, more multi-discussing behaviors tend to bring longer issue resolution latency. We also conduct the qualitative analysis to explore the developers’ experiences and expectations of multi-discussing.

Conversational large-language models (LLMs), such as ChatGPT, are extensively used for issue resolution tasks, particularly for generating ideas to implement new features or resolve bugs. However, not all developer-LLM conversations are useful for effective issue resolution and it is still unknown what makes some of these conversations not helpful. In this paper, we analyze 686 developer-ChatGPT conversations shared within GitHub issue threads to identify characteristics that make these conversations effective for issue resolution. First, we empirically analyze the conversations and their corresponding issue threads to distinguish helpful from unhelpful conversations. We begin by categorizing the types of tasks developers seek help with (e.g., code generation , bug identification and fixing , test generation ), to better understand the scenarios in which ChatGPT is most effective. Next, we examine a wide range of conversational, project, and issue-related metrics to uncover statistically significant factors associated with helpful conversations. Finally, we identify common deficiencies in unhelpful ChatGPT responses to highlight areas that could inform the design of more effective developer-facing tools. We found that only 62% of the ChatGPT conversations were helpful for successful issue resolution. Among different tasks related to issue resolution, ChatGPT was most helpful in assisting with code generation, and tool/library/API recommendations, but struggled with generating code explanations. Our conversational metrics reveal that helpful conversations are shorter, more readable, and exhibit higher semantic and linguistic alignment. Our project metrics reveal that larger, more popular projects and experienced developers benefit more from ChatGPT’s assistance. Our issue metrics indicate that ChatGPT is more effective on simpler issues characterized by limited developer activity and faster resolution times. These typically involve well-scoped technical problems such as compilation errors and tool feature requests. In contrast, it performs less effectively on complex issues that demand deep project-specific understanding, such as system-level code debugging and refactoring. The most common deficiencies in unhelpful ChatGPT responses include incorrect information and lack of comprehensiveness. Our findings have wide implications including guiding developers on effective interaction strategies for issue resolution, informing the development of tools or frameworks to support optimal prompt design, and providing insights on fine-tuning LLMs for issue resolution tasks.

Social coding facilitates the sharing of ideas within and between projects in an open source ecosystem. Bug fixing and triaging, in particular, are aided by linking issues in one project to potentially related issues within it or in other projects in the ecosystem. Identifying and linking to related issues is in general challenging, and more so across projects. Previous studies, on a limited number of projects have shown that linking to issues within a project associates with faster issue resolution times than cross-project linking. In this paper, we present a mixed methods study of the relationship between the practice of issue linking and issue resolution in the Rails ecosystem of open source projects. Using a qualitative study of issue linking we identify a discrete set of linking outcomes together with their coarse-grained effects on issue resolution. Using quantitative study of patterns in developer linking within and across projects, from a large-scale dataset of issues in Rails and its satellite projects, we find that developers tend to link more cross-project or cross-ecosystem issues over time. Furthermore, using models of issue resolution latency, when controlled for various attributes, we do not find evidence that linking across projects will retard issue resolution, but we do find that it is associated with more discussion.

Addressing issue reports is an integral part of open source software (OSS) projects. Although several studies have attempted to discover the factors that affect issue resolution, few pay attention to first responders, who is the first to post their comments after an issue report is published. We are interested at how first responders affect issue resolution process for OSS projects. Therefore, we extract the data from Github to perform our empirical study. By obtaining their identity types and speech acts, we analyze the impact of first responders with different identity types on the efficiency of issue resolution based on three metrics and find that identified users especially collaborators make first response can improve the efficiency of issue resolution. Furthermore, we make use of the identity type information of the first responders to forecast the issue lifetime and the results show that this information can also improve the accuracy for short-term prediction. It also verifies that first responders have a direct influence on the issue resolution process.

Comments are beneficial for developers to understand and maintain the code in software development life cycle. Well-commented code can generally help developers to resolve issues efficiently. Due to the complexity of code implementation, code comments may be generated to represent different types of information. And it is hard to keep all the code well-commented in real-world projects. In this case, it is meaningful to investigate how the different types of comments impact the resolution of issues. Then we can maintain the code comments purposefully, and we can also provide some suggestions for the comment generation techniques. To analyze the efforts of different comments on issue resolution, we classify code comments into two categories, i.e., functionality-aspect and non-functionality-aspect comments. In this paper, we analyze the effects of 53k pieces of code comments on the issues from 10 open-source projects within a period of 24 months. The results show that the majority of code comments are used to represent the functionality, e.g., the summary and purpose of code. Nevertheless, the other non-functionality-aspect comments have much stronger correlation with the resolution of software issues. For the resolved patches, the non-functionality-aspect comments are more frequently to be updated or added than the functionality-aspect comments. These findings confirm the important role of non-functionality-aspect comments during issue resolution, although their proportion is far less than that of functionality-aspect comments.

Social coding sites like GitHub has enabled developers to easily contribute their comments on multiple issues and switch their discussion between issues, i.e., multi-discussing. Discussing multiple issues simultaneously may enhance the work efficiency of developers. However, multi-discussing also relies on developers' rationally allocating their time and focus, which may bring different influence to the resolution of issues. Therefore, investigating how multi-discussing affects the issue resolution is a meaningful research question which can help developers understand the benefits and limitations when they switch their discussion between issues. In this paper, we present a preliminary study of the impact of multi-discussing on issue resolution in GitHub projects, by using quantitative methods. First, we collect and analyzed data from 631 GitHub projects to explore how multi-discussing affects the average resolution latency of project issues. Further, we develop method for measuring the rate and breadth of a developers' discussionswitching behavior, and we use regression modeling to study how discussion-switching affects the single issue resolution latency. We find that multi-discussing is a common behavior of developers in GitHub projects. Also, multi-discussing is associated with shorter average issue resolution latency of project. However, during a single issue resolution, more participants' discussion-switching tend to bring longer issue resolution latency. Our study motivates the need for further research on the multi-discussing.

This study focuses on the independent and interactive effects of problem issue situations and resolution strategies on performance in manufacturer–dealer channel relationships. Conflict issue situation and resolution strategy were a priori matched for a 2 × 2 factorial quasi-experiment. The theoretical framework of (inter)organizational conflict reaction theory was used to extend existing empirical findings about the influence of two factors that foster constructive or destructive consequences experienced from the prospective of dealers. Data were obtained from a sample of 243 dealers and 9 trade administrative experts. The results indicate significant main effects of issue situation and resolution strategy on dealer performance. A significant interaction effect also was found. Managerial implications for dealing with ongoing problem issues with different resolution strategies are discussed.

The study focused on media cartoons as a teaching strategy in Environmental Education. Specifically, it sought to determine the effects of media cartoons on the issue resolution skills of first year high school students. The study was conducted in La Salle Green Hills that had eleven sections in the first year high school level for the School Year 2009-2010. Two comparable sections being taught by the researcher were chosen as the groups for the study. Both classes met for 80 minutes per meeting, three times a week. The students were given a pretest and a posttest on both Issue Resolution Skills Test (IRST). The IRST measured the ability to provide solutions to various environmental issues and problems. A teaching strategy that included film showing, group dynamics, laboratory activities, and motivational games was utilized for the conventional group. Media cartoons that improve skills in issue resolution and conceptual understanding of topics on Environmental Education were introduced to the students in the experimental group.The scores in the pretest and posttest of the participants were tabulated and used to determine the significant difference of the students’ mean performance in the media cartoons and conventional groups. The t-test was utilized in the treatment and analysis of data gathered. Findings reveal that exposure to media cartoons results to a significantly better issue resolution skills on environmental education topics than the conventional approach. The researcher observed that students actively engaged themselves in media cartoon activities that enabled them to make responsible actions and provide solutions to local and global environmental problems. Students had an active participation in sharing insights and opinions in evaluating the message of media cartoons. Based on the findings of this study, the researcher concludes that exposure to media cartoons significantly improves the issue resolution skills of students. The strategy provided a learning opportunity in a non-threatening setting that promotes students’ skills of observation, formulation of hypothesis, and creativity. In this regard, the researcher encourages the use of media cartoons as an alternative teaching strategy as it improved the issue resolution skills of students. Learning activities in combination with environmental education methods can greatly enhance students’ engagement with environmental and science issues. Normal 0 21 false false false TR X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:Normal Tablo; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin:0cm; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:11.0pt; font-family:Calibri,sans-serif; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:Times New Roman; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:Times New Roman; mso-bidi-theme-font:minor-bidi;}

Agile project management, focusing on flexibility and iterative delivery, has a tendency to view issue resolution timing as vague and thus has a tendency to result in schedule overruns and resource planning inefficiency. This research contributes a large-scale statistical approach to predicting issue resolution patterns from historical JIRA data, providing quantitative analysis to the project managers to enhance sprint planning and resource allocation. It utilizes time-series analysis with a broad dataset of 1,095 days of issue tracking record sourced from a publicly available Kaggle dataset, being actual software development projects. It uses an Auto Regressive Integrated Moving Average (ARIMA) model that effectively tests various setups of parameters using stringent statistical tests. The optimal ARIMA (1,1,1) model demonstrated strong forecasting capability, as indicated by the performance metrics: AIC (Akaike Information Criterion) = 1872.52, BIC (Bayesian Information Criterion) = 1887.07, & extremely low error metrics (Mean Absolute Error = 0.0006525, Root mean squared Error = 3.2501). The results validate the efficiency of the model for forecasting issues resolved per day and establishing patterns over time in team productivity. This research provides robust variations in resolution rate prediction across development cycle stages, with high performance in stable sprints. The model output shows good estimation of team capacity for data-driven sprint backlogs and deadline realignment. This research adds a practical, scalable approach for JIRA-using teams, bridging the gap between Agile principles and data science.

The “The Nexikon Society” is an offers an integrated platform aimed at facilitating effective communication, information sharing, and issue resolution within residential communities. This platform serves as a centralized hub where society members can access real-time updates, notices, and event announcements, fostering a stronger sense of community engagement and collaboration. Through the Nexikon website, residents have easy access to various community-related information, including updates on ongoing projects, upcoming events, and important notices. Additionally, the platform allows members to view profiles of their neighbour’s, facilitating networking and social interactions within the community. A significant feature of Nexikon is its complaint management system, which enables members to report issues or concerns pertaining to the society. This feature ensures that grievances are addressed promptly and promotes transparency and accountability among society administrators. Moreover, the website restricts database modification privileges to the administrator, ensuring the integrity and security of the data. The administrator is tasked with managing member information, overseeing platform operations, and performing essential tasks to ensure its smooth functioning. Keywords— Society Management System, Residential Communities, Communication, Information Dissemination, Issue Resolution, Administrator, Database Management.

We performed an empirical study of the relation between technical quality of software products and the issue resolution performance of their maintainers. In particular, we tested the hypothesis that ratings for source code maintainability, as employed by the Software Improvement Group (SIG) quality model, are correlated with ratings for issue resolution speed. We tested the hypothesis for issues of type defect and of type enhancement. This study revealed that all but one of the metrics of the SIG quality model show a significant positive correlation with the resolution speed of defects, enhancements, or both.

Many developer recommendation techniques have been developed in the literature. Among existing studies, most of them are performed based on exploring the historical commit repository. The thought behind them is that developers who submit similar historical commits relevant to the incoming issue are more probably to be the candidates for the current issue resolution. But whether such a thought is always useful for developer recommendation? This paper aims at this problem by conducting a set of empirical studies on four real open-source projects. The results show that, 1) historical commit messages do well reflect the historical experience of the maintenance task of developers and can be used for developer recommendation in most of the time; 2) the number of historical commits submitted by the recommended developer(s) and the similarity value used to select the relevant historical commits should be carefully considered to recommend developers for issue resolution; 3) The efficiency of issue resolution process can be improved if some associated source code files relevant to this issue can be also recommended; and 4) developer recommendation techniques that rank the recommended developers based on the times of co-changed source code files cannot always produce correct recommendation results.

Although remote working is increasingly adopted during the pandemic, many are concerned by the low-efficiency in the remote working. Missing in text-based communication are non-verbal cues such as facial expressions and body language, which hinders the effective communication and negatively impacts the work outcomes. Prevalent on social media platforms, emojis, as alternative non-verbal cues, are gaining popularity in the virtual workspaces well. In this paper, we study how emoji usage influences developer participation and issue resolution in virtual workspaces. To this end, we collect GitHub issues for a one-year period and apply causal inference techniques to measure the causal effect of emojis on the outcome of issues, controlling for confounders such as issue content, repository, and author information. We find that emojis can significantly reduce the resolution time of issues and attract more user participation. We also compare the heterogeneous effect on different types of issues. These findings deepen our understanding of the developer communities, and they provide design implications on how to facilitate interactions and broaden developer participation.

This paper presents a methodology for issue resolution in conceptual design. Conceptual design is the preliminary phase of design in which both well-defined problem specifications and high level design solutions are developed. It has been shown that eighty five percent of the lifecycle costs are determined during the conceptual design phase in the development of a product. Generally there are three modules: problem definition, issue resolution, and conceptual formation in the architecture of conceptual design. Problem definition generates a problem description, a set of requirements, and/or a set of design constraints. After the problem is defined, consultation with professionals may be necessary to find out the actual corresponding issues for the design. A more objective approach for convincing participators of the correctness of the chosen issues is to use data. After the design issues are identified the conflicts among them need to be resolved in the issue resolution module. The proposed approach provides a methodology for designers to determine the solutions to the appropriate issues that fit in with the problem definition and improves the efficiency of conceptual design process. It is based on an intuitively appealing methodology, the analytic hierarchy process (AHP). Our approach has been applied to magnesium alloys industry for the conceptual design of WIP containers. The conflicts among the identified issues are resolved successfully after applying it. The real world case demonstrates the practicability and efficiency of the proposed methodology.

Service issue escalation and resolution in cloud enabled enterprise case management are difficult to govern in large insurance portfolios, where tickets can trigger avoidable escalation, stalled ownership, and inconsistent closure. This study developed and tested a data driven framework linking escalation criteria standardization (ECS), workflow automation support (WAS), cross functional coordination (CFC), data quality (DQ), analytics effectiveness (AE), and governance and accountability (GOV) to escalation effectiveness (EE) and resolution performance (RP). Using a quantitative, cross sectional, case-based design, a five-point Likert survey captured perceptions from 228 employees in one enterprise portfolio using a cloud-based case management workflow (frontline 42.1%, specialists 28.5%, supervisors 19.3%, QA or support 10.1%). Analysis included data screening, Cronbach reliability, descriptive statistics, Pearson correlation, two multiple regression models, and a mediation test. Internal consistency was high (alpha 0.83 to 0.91). Mean scores indicated moderate capability but uneven execution (3.42 to 4.11), and pathway integrity was weakest on documentation completeness (M 3.41) and ownership continuity (M 3.38) compared with routing accuracy (M 3.79). Correlations supported key links, including ECS with EE (r 0.56), WAS with EE (r 0.49), CFC with EE (r 0.52), DQ with AE (r 0.58), AE with RP (r 0.61), EE with RP (r 0.57), and GOV with RP (r 0.46), all with p below 0.001. Regression results showed ECS (beta 0.31), WAS (beta 0.24), and CFC (beta 0.29) explained 54% of variance in EE (R2 0.54), while AE (beta 0.33), EE (beta 0.28), GOV (beta 0.19), and DQ (beta 0.17) explained 62% of RP (R2 0.62). EE partially mediated ECS to RP, with the direct effect decreasing from beta 0.34 to 0.21 and an indirect effect of 0.13 (p below 0.01). These findings highlight actionable levers for service leaders and BI governance. Implications are that teams can improve closure speed and durability by enforcing complete handoff packages, expanding automated routing and aging controls, and using analytics dashboards under clear ownership rules.