Open source projects often incorporate similar principles and practices as Agile and Lean software development projects. Contrary to traditional organizations, the environment in which these projects function has an impact on process-related elements like the flow of work and value. Process metrics are typically employed during Agile Software Engineering projects as a means of providing meaningful feedback. This paper presents an exploratory study investigating whether these metrics can be beneficial to open source communities This knowledge will assist in educating open source ecosystems about the applications of agile software engineering and its variations in open source projects. We analyzed the GitHub project boards of 5 mature and large-scale open source projects, applying multiple agile/lean process metrics to explore whether interesting patterns emerged. The methodology used to collect these metrics and relevant findings are discussed in this paper. The original contribution of this research is to determine whether agile/lean process metrics are helpful in open source, as well as the opportunities and obstacles that may arise when applying agile and lean principles to open source software development.

Context:- Regression testing in agile and continuous integration settings must support fast feedback while operating within limited testing windows. Test selection can reduce execution effort, but the selection process itself also consumes time before tests can run. Objectives:- This study investigates whether the decision cost of regression test selection can be reduced without degrading the quality of the selected test subsets. The focus is on preserving the quality of the selected subset while reducing the time required to reach a selection decision. Methods:- We conducted a controlled simulation study comparing a baseline regression test selector with compute-aware implementations that avoid redundant reassessment of candidate test subsets. The implementations were evaluated across constrained regression testing windows using datasets with different requirement-test structures. The assessment considered business value, requirement coverage, and the time required for selection decisions. Results:- The compute-aware implementations preserved selected-subset quality once sufficient assessment effort was available. When the assessment effort was too limited, the baseline selector could retain an early quality advantage. After quality stabilized, additional assessment mainly increased selection decision time rather than improving the selected test subsets. The point at which this stabilization occurred depended on the structure of the requirement-test mapping. Conclusion:- Regression test selection should account for both the quality of the subset and the time required to select the tests. Reducing redundant assessment can lower decision cost while maintaining practical selection quality, provided that enough assessment effort is allocated. The results support treating selection effort as a budget-calibration problem within agile and continuous integration feedback cycles.

In agile software development, regression testing is an ongoing process activity. However, real-world time constraints necessitate selecting a subset of tests to run. Current regression test selection algorithms primarily focus on technical metrics such as requirement coverage while overlooking the business value each test validates. This study reframes regression test selection as a multi-objective optimization problem in which tests are selected to maximize business value within a constrained testing time while maintaining adequate requirement coverage. We apply an artificial intelligence-based search algorithm, and our results show that the proposed method consistently selects tests with higher business value than baseline approaches when time is limited, while maintaining comparable requirement coverage. These findings suggest that a driven value-aware selector can be incorporated into agile teams for decisions on allocating limited regression testing resources.

Software developed using modern agile practices delivers a stream of software versions that require continuous regression testing rather than testing once close to the delivery or maintenance phase, as assumed by classical regression-testing theory. In this work, we formalize the phenomenon of continuous or near-continuous regression testing using successive builds as a time-ordered chain, where each build contains the program, requirements, and the accompanying tests. We also formalize the regression test window between any two builds, which captures the limited time budget available for regression testing. As the time limit is set to infinity and the chain is closed to two builds, the model degenerates to retest-all, thereby preserving semantics for the classical two-version case. The formalization is validated by directly representing two state-of-the-art agile regression testing algorithms in terms of build-tuple operations without requiring auxiliary assumptions, followed by proof of the soundness and completeness of our formalization.

This paper introduces an intelligent value-driven approach to regression test selection to enhance the effectiveness of regression testing in agile development. By selecting test cases associated with higher business value, this approach ensures that critical existing business functionalities are thoroughly retested in case of any changes during the testing process. Unlike traditional approaches focusing on technical coverage, this method integrates business priorities, resulting in a more targeted and strategic test selection. Simulations comparing this approach to random selection demonstrate that it preserves more business value while reducing the number of test cases executed. The results indicate that this value-driven regression testing selection method strikes an optimal balance between testing efficiency and safeguarding high-impact features, making it a practical and effective solution for agile environments.

Background: Regression testing is critical in agile software development, as it ensures that frequent changes do not introduce defects into previously working functionalities. While agile methodologies emphasize rapid iterations and value delivery, regression testing research has predominantly focused on optimizing technical efficiency rather than aligning with agile principles. Aim: This study aims to systematically map research trends and gaps in regression testing within agile environments, identifying areas that require further exploration to enhance alignment with agile practices and value-driven outcomes. Method: A systematic mapping study analyzed 35 primary studies. The research categorized studies based on their focus areas, evaluation metrics, agile frameworks, and methodologies, providing a comprehensive overview of the field. Results: The findings strongly emphasize test prioritization and selection, reflecting the need for optimized fault detection and execution efficiency in agile workflows. However, areas such as test generation, test minimization, and cost analysis are under-explored. Current evaluation metrics primarily address technical outcomes, neglecting agile-specific aspects like defect severity’s business impact and iterative workflows. Additionally, the research highlights the dominance of continuous integration frameworks, with limited attention to other agile practices like Scrum and a lack of datasets capturing agile-specific attributes such as testing costs and user story importance. Conclusions: This study underscores the need for research to expand beyond existing focus areas, exploring diverse testing techniques and developing agile-centric metrics and datasets. By addressing these gaps, future work can enhance the applicability of regression testing strategies and align them more closely with agile development principles.

Software industry is a fast-moving industry and to keep up with this pace the development process also needs to be fast and efficient and Agile software development (ASD) is the answer to this problem. Even though ASD has been in there for over two decades there are still multiple unknown questions tied to ASD that need to be addressed. In this study we are going to address one of the most critical factors of ASD i.e. Communication. We conducted a review of 14 studies and found the areas under ASD communication that the community is interested in as well as research gaps.

Machine learning and AI have been recently embraced by many companies. Machine Learning Operations, (MLOps), refers to the use of continuous software engineering processes, such as DevOps, in the deployment of machine learning models to production. Nevertheless, not all machine learning initiatives successfully transition to the production stage owing to the multitude of intricate factors involved. This article discusses the issues that exist in several components of the MLOps pipeline, namely the data manipulation pipeline, model building pipeline, and deployment pipeline. A systematic mapping study is performed to identify the challenges that arise in the MLOps system categorized by different focus areas. Using this data, realistic and applicable recommendations are offered for tools or solutions that can be used for their implementation. The main value of this work is it maps distinctive challenges in MLOps along with the recommended solutions outlined in our study. These guidelines are not specific to any particular tool and are applicable to both research and industrial settings.

The extraordinary success of agile software development in small software teams pushed organizations to find ways to scale the practices to large projects and multiple teams. However, agile works differently in the large than in the small. This paper assimilates organizational challenges and success factors for large-scale agile transformations. We further explore these factors in their business domain and the transformation framework implemented. We conducted a systematic literature review of 15 case studies and found that organizations may share common challenges and success factors. However, agile’s challenges and success factors are very case-specific, and what works for one may not work for the other, even when both are in the same business domain or following the same framework.

Social media is a pervasive platform for delivering targeted interventions, albeit with cautionary ethical consequences. Recently AI robots or "bots" have been combined with social media platforms to enhance interaction, and enact behavior change through increased engagement and adherence to intervention protocols. This paper presents a customized social media platform for promoting engagement and adherence to a prevention intervention protocol. The protocol was originally developed in a group workshop format, and then online during COVID-19. A social media platform was utilized to connect group participants and deliver protocol activities. Bots encouraged participation via positive reinforcement mechanism for the entire group, and to remind a participant of protocol activities. While not a formal study, our exploratory results demonstrate that bots and a social media context support a group leader in increased engagement and adherence to the protocol. Our principal contribution in this paper is demonstrating that a personalized, adaptive instance of a Control Systems Engineering model may improve engagement-related outcomes in brief protocols.

Mobile health applications have the potential for addressing chronic health conditions, but challenges exist in adherence, or the extent to which a patient conducts the activities defined in a clinical protocol. High levels of adherence should lead to greater effects of the intervention; the greater fidelity to the protocol, the more benefit one should receive from the protocol. Further, this delivery mechanism supports just-in-time “micro” interventions, or smaller yet more frequent dosages of skill practice. mHealth has limitations in these areas; the ability of patients to sustainably adhere to a protocol, and to drive intervention effect sizes. This research considers personalized just-in-time adaptive micro-interventions as a potential remedy to these limitations. Specifically, in the context of a pediatric anxiety protocol, we introduce an approach to drive greater levels of adherence and effect sizes by incorporating per-patient information. This approach has been implemented within an mHealth app for middle school that was successfully pilot-tested in the Phoenix area. The number of users is small (n=3) so a case-by-case analysis of app usage is presented. Simulated user behaviors based on models of adherence and effect sizes over time are presented as a means to demonstrate the potential impact of personalized deployments on a larger scale.

Online education is rapidly being adopted in major universities. Software engineering programs are early adopters among engineering disciplines due to the ability to leverage advances in cloud computing and personal computing environments to support software engineering environments. Online modalities have several benefits, but present challenges as well. This extended abstract suggests that the continuing rise of online education combined with the increases in software engineering enrollments create unique challenges for software engineering educators, chief among them the ability to effectively utilize project-centric pedagogy to contextualize learning experiences for our students.

Online education is rapidly being adopted in major universities. Software engineering programs are early adopters among engineering disciplines due to the ability to leverage advances in cloud computing and personal computing environments to support software engineering environments. Online modalities have several benefits, but present challenges as well. This extended abstract suggests that the continuing rise of online education combined with the increases in software engineering enrollments create unique challenges for software engineering educators, chief among them the ability to effectively utilize project-centric pedagogy to contextualize learning experiences for our students.

The semantic web is a mesh of information linked in a way that can be easily shared and reused to make inferences for the end user. The semantic web attempts to find and access web sites and web resources not by keywords but by descriptions of their contents and capabilities. This has been made possible by adding structure to the content of web pages and developing an environment where software agents can perform sophisticated functions for users. Semantic web customization of JMaPSS uses the Java Marker Passing Search System (JMaPSS) which applies a spreading activation search algorithm known as marker passing to significantly improve search results. This research project focuses on visualizing the semantic network and displaying marker propagation to distinctively illustrate various elements of the semantic web ontology in the form of a graph structure. The tool displays the results of marker propagation by highlighting the active nodes and the propagation path.

One of the most common errors developers make is to provide incorrect string identifiers across the HTML5-JavaScript-CSS3 stack. The existing literature shows that a significant percentage of defects observed in real-world codebases belong to this category. Existing work focuses on semantic static analysis, while this paper attempts to tackle challenges that can be solved using syntactic static analysis. While semantic state analysis is more powerful, it creates a greater computational burden on tool processing while simple static analysis may be computed faster, allowing for better integration in inline syntax-highlighting marker in a user interface or a quick pass through large codebases. This paper proposes a tool for quickly identifying defects at the time of injection due to dependencies between HTML5, JavaScript, and CSS3, specifically in syntactic errors in string identifiers. The proposed solution reduces the delta (time) between defect injection and discovery with the use of a dedicated just-in-time syntactic string identifier resolution tool. The solution focuses on modeling the nature of syntactic dependencies across the stack, and providing a tool that helps developers discover such dependencies. This tool was validated against a set of real-world codebases to analyze the significance of these defects.

A significant problem in Massive Open Online Courses (MOOCs) is the high rate of student dropout in these courses. An effective student dropout prediction model of MOOC courses can identify the factors responsible and provide insight on how to initiate interventions to increase student success in a MOOC. Different features and various approaches are available for the prediction of student dropout in MOOC courses. In this paper, the data derived from a self-paced math course, College Algebra and Problem Solving, offered on the MOOC platform Open edX partnering with Arizona State University (ASU) from 2016 to 2020 is considered. This paper presents a model to predict the dropout of students from a MOOC course given a set of features engineered from student daily learning progress. The Random Forest Model technique in Machine Learning (ML) is used in the prediction and is evaluated using validation metrics including accuracy, precision, recall, F1-score, Area Under the Curve (AUC), and Receiver Operating Characteristic (ROC) curve. The model developed can predict the dropout or continuation of students on any given day in the MOOC course with an accuracy of 87.5%, AUC of 94.5%, precision of 88%, recall of 87.5%, and F1-score of 87.5%, respectively. The contributing features and interactions were explained using Shapely values for the prediction of the model.

Due to the fast-paced nature of the software industry and the success of small agile projects, researchers and practitioners are interested in scaling agile processes to larger projects. Agile software development (ASD) has been growing in popularity for over two decades. With the success of small-scale agile transformation, organizations started to focus on scaling agile. There is a scarcity of literature in this field making it harder to find plausible evidence to identify the science behind large scale agile transformation. The objective of this paper is to present a better understanding of the current state of research in the field of scaled agile transformation and explore research gaps. This tertiary study identifies seven relevant peer reviewed studies and reports research findings and future research avenues.

This Work-in-Progress Innovative Practice paper presents the design, environment, and preliminary results of implementing a virtualized environment for an upper-division course offering in Software Engineering. The Internet-of-Things (IoT) is an emerging paradigm rapidly gaining adoption in everyday consumer computing. Several recent publications and educational platforms recognize the potential for IoT to serve as this rich platform for computing education. However, most of these contributions focus on early undergraduate courses in small hands-on settings using general purpose hobbyist kits based on Arduinos or Raspberry PIs. This paper presents a new and novel IoT educational platform based on virtualization technology. The platform was designed specifically for scalable and complex IoT systems-oriented challenges appropriate for upper-division undergraduate study. This platform was utilized in an online setting at Arizona State University for the first time in Fall 2019. This paper presents insights from this experience, based on student in-class surveys, formal course evaluations, and the instructor’s perspective, along with a roadmap for maturing the platform.

This paper describes the impact of an agile process support environment in helping faculty manage software engineering capstone projects and the learning outcomes associated with the capstone experience.Software engineering capstone projects are notoriously time-consuming to manage for faculty mentors.Team projects often fall behind due to the inexperience of the students and the external pressures they face.They may be accustomed to performing heroic acts on prior individual class projects, and think they can be successful this way again.But in a significantly sized real-world team project, they find out too late that this approach will not work.Students remain successful often by significant effort on the part of a faculty mentor.The mentor may setup a process infrastructure to enables project monitoring.Mentors may find themselves asking for frequent in-class project reviews, out-of-class appointments, and significant documentation.Mentoring a capstone project, while a potentially rewarding experience, can become a significant time sink and lead to faculty burnout.We are utilizing the IBM Jazz environment including the Rational Team Concert (RTC) integrated development environment (IDE) to address project management for capstone projects using the Agile/Scrum methodology.Jazz/RTC allows all stakeholders (students, sponsors, and faculty) to transparently review a process to assess project health at any point in time.Further, transparent continuous project monitoring gives mentors the ability to provide just-in-time-but-not-too-late formative feedback, as well as allow continuous assessment of learning outcomes.The ability to "see where you are" in the process, and understand how the process' practices drive progress and completion, is an invaluable learning aid for students struggling to grasp the benefits of these methods.