This study conducts a comparative analysis of Artificial Intelligence (AI) software and traditional GitHub repositories, focusing on workflow efficiency and sentiment dynamics.Using process mining and sentiment analysis techniques, we examine repositories from eight prominent projects, encompassing diverse datasets of issues and pull requests filtered for relevance and consistency.Our findings reveal that AI software repositories exhibit different workflow patterns and sentiment dynamics compared to traditional repositories.Sentiment analysis uncovers that contributors to AI software repositories experience more positive sentiment dynamics, likely reflecting structured workflows and collaborative tools.Conversely, traditional repositories exhibit longer resolution times and more fluctuating sentiment patterns, which may indicate higher complexity or less automation.These insights provide valuable recommendations for optimizing repository management, fostering contributor satisfaction, and improving collaborative software development environments.

ABSTRACT Context With the fast advancement of techniques in artificial intelligence (AI) and of the target infrastructures in the last decades, AI software is becoming an undeniable part of software system projects. As in most cases in history, however, development methods and guides follow the advancements in technology with phase differences. Purpose With an aim to elicit and integrate available evidence from AI software development practices into a process model, this study synthesizes the contributions of the validation studies reported in scientific literature. Method We applied a systematic literature review to retrieve, select, and analyze the primary studies. After a comprehensive and rigorous search and scoping review, we identified 82 studies that make various contributions in relation to AI software development practices. To increase the effectiveness of the synthesis and the usefulness of the outcome, for detailed analysis, we selected 14 primary studies (out of 82) that empirically validated their contributions. Results We carefully reviewed the selected studies that validate proposals on approaches/models, methods/techniques, tasks/phases, lessons learned/best practices, or workflows. We mapped the steps/activities in these proposals with the knowledge areas in SWEBOK, and using the evidence in this mapping and the primary studies, we synthesized a process model that integrates activities, artifacts, and roles for AI‐enabled software system development. Conclusion To the best of our knowledge, this is the first study that proposes such a process model by eliciting and gathering the contributions of the validation studies in a bottom‐up manner. We expect that the output of this synthesis will be input for further research to validate or improve the process model.

Abstract Agile software development has reached wide adoption in various countries including Turkey, even though from which its original cultural backgrounds differ. In Turkey, many organizations have started to adopt Agile approaches more and more in their software development processes. This interest in the country's software development is parallel to what the academic literature on Agile in the country exhibits. However, despite the prevalence of Agile in Turkey, there is a lack of sufficient secondary research and comprehensive review on Agile in Turkey, which poses a significant necessity for further investigation. Considering this gap, we performed a quantitative bibliometric analysis of Agile software development publications produced by Turkish organizations in a holistic and broad approach both for scholars and practitioners. We provide a summary of relevant academic studies that emerged in Agile research in Turkey by focusing on many aspects including bibliometric properties of papers, researchers, affiliations, venues, and thematic contents that are separated into 15 sub‐research questions. After delivering results based on the questions, we discuss the results and findings of our study and present implications regarding the findings. The main contributions of our work are twofold. First, the paper may help the readers to have a quick idea, understand the subject, and gain insight from a large volume of scientific data. Second, the paper can help readers to use these analyses to form future research.

Abstract4Among the Agile methods, Scrum and Kanban are widely used in software development and they are considered the two most effective ones influencing the direct results of projects.Despite the importance of knowing their relative strengths and advantages and integrating them to achieve better results than individual use, none of the secondary studies provide extensive knowledge on the topic.In this paper, we performed a Systematic Literature Review (SLR) study to investigate the characteristics of the empirical studies which involve Scrum and Kanban by comparing or integrating them.Our final set includes 38 studies posing primary information on the advantages of each method over another one, the properties including artifacts, roles, and events from Scrum and Kanban in combining them in a hybrid way, and the properties of transitions from one to another such as transition directions (Scrum to Kanban, Kanban to Scrum or Scrum/Kanban to Hybrid), transition years, and transition reasons.The outputs can be interesting for both industry and researchers.For example, nearly all of the transitioning organizations are moving from Scrum to Kanban or to hybrid method.Among the reasons for the transitions, the problems experienced with Scrum are remarkable.In comparison, Kanban stands out clearly in a positive way.Almost all of the teams combining both use flow instead of a sprint.

Process mining mainly focuses on discovering control flow models, conformance checking and analyzing bottlenecks. It extends the scope by looking at the other perspectives like time, data and resources by connecting events in the event logs to this process model. These perspectives are not isolated and are all related to each other. For each perspective, there is a different technique, which is dedicated to the relevant perspective, applied and these techniques may need to consume the results of one another in a sequence of process mining analyses. As a result, a holistic process model is created by attaching and binding related attributes of the event logs to the backbone (control flow) of the model. Therefore, representing the holistic model and keeping what is produced from each perspective in a secure and immutable way while applying the multiple perspectives become important. In this study, a BPMN-extended Data Model is proposed to put together the models from the multi-perspective process mining and a tool is developed to keep this data model as an asset into a private blockchain developed by using Hyperledger Fabric. The practical relevance and validity of the approach are shown in the case studies that use real-life data from two different domains.

Software development with its unique characteristics having knowledge-intensive and human-oriented aspects and complex domains, challenges organizations. The timely outcomes with high quality and desired cost that directly affect customer satisfaction have an important place in many organizations, including banks. In the last decade, as an emerging technique for business processes management, process mining has been applied in many domains, including manufacturing, supply chain, government, healthcare, and software engineering. There are limited number of studies on process mining techniques carried out for the software process, especially in the banking sector. A lack of tool infrastructure enabling to run the entire software development process and the challenges in integrating processed data from separated varying tools and assets complicate the use of process mining for software processes. This paper aims to identify the improvement points in the software development process of the Kuveyt Turk Participation Bank in Turkey through the surfacing actions. The findings and results are gathered by the application of process mining techniques of bupaR, and evaluation is provided by experts in the bank. After that, the relevant process improvements are identified. The results of this paper show that using process mining provides the organization with beneficial results, in particular, and a comprehensive view of the end-to-end Software Development Life Cycle (SDLC) processes.

Process mining (PM) as applied in different industrial settings is considered as an effective technique for business process analytics. In healthcare domain, applications of this technique are yet limited but emerging. Process conformance verification, which is applied to measure differences between process model specification and process enactments, is one of the basic reasons to use the PM technique. The differences highlight the gaps to improve the process model specification by using the information gained from the process enactments. This paper first provides an overview of the studies that use the PM technique for conformance verification of healthcare processes as elicited from a systematic mapping study. It then describes the features of a tool that will use the PM technique to carry out process analytics in an intensive care unit of a university hospital.