Autonomous coding agents (e.g., OpenAI Codex, Devin, GitHub Copilot) are increasingly used to generate fix-related pull requests (PRs) in real-world software repositories. However, their practical effectiveness depends on whether project maintainers accept and merge these contributions. In this paper, we present an empirical study of AI agent–involved fix-related PRs, examining both their integration outcomes, latency, and the factors that hinder successful merging. We first analyze 8,106 fix-related PRs authored by five widely used AI coding agents from the AIDEV-POP dataset to quantify the proportions of PRs that are merged, closed without merging, or remain open. We then conduct a manual analysis of a statistically significant sample of 326 closed but unmerged PRs, spending approximately 100 person-hours to construct a structured catalog of 12 failure reasons. Our results indicate that test case failures and prior resolution of the same issues by other PRs are the most common causes of non-integration, whereas build or deployment failures are comparatively rare. Overall, our findings expose key limitations of current AI coding agents in real-world settings and highlight directions for their further improvement and for more effective human-AI collaboration in software maintenance.

Scientific workflow has become essential in software engineering because it provides a structured approach to designing, executing, and analyzing scientific experiments. Software developers and researchers have developed hundreds of scientific workflow management systems so scientists in various domains can benefit from them by automating repetitive tasks, enhancing collaboration, and ensuring the reproducibility of their results. However, even for expert users, workflow creation is a complex task due to the dramatic growth of tools and data heterogeneity. Thus, scientists attempt to reuse existing workflows shared in workflow repositories. Unfortunately, several challenges prevent scientists from reusing those workflows. Thus, we first attempted to identify those reusability challenges in this study. We also offered an action list and evidence-based guidelines to promote the reusability of scientific workflows. Our intensive manual investigation examined the reusability of existing workflows and exposed several challenges. The challenges preventing reusability include tool upgrading, tool support unavailability, design flaws, incomplete workflows, failure to load a workflow, etc. Such challenges and our action list offered guidelines to future workflow composers to create better workflows with enhanced reusability. In the future, we plan to develop a recommender system using reusable workflows that can assist scientists in creating effective and error-free workflows.

Design optimization of an unmanned underwater vehicle (UUV) is a complex and a computationally expensive exercise that requires the identification of optimal vehicle dimensions offering the best tradeoffs between the objectives, while satisfying the set of design constraints. Although hull form optimization of marine vessels has long been an active area of research, limited attempts in the past have focused on the design optimization of UUVs and there are even fewer reports on the use of high-fidelity analysis methods within the course of optimization. While it is understood that the high-fidelity analysis is more accurate, they also tend to be far more computationally expensive. Thus, it is important to identify when a high-fidelity analysis is required as opposed to a low-fidelity estimate. The work reported in this paper is an extension of the authors previous work of a design optimization framework, where the design problem was solved using a low-fidelity model based on empirical estimates of drag. In this paper, the framework is extended to deal with high-fidelity estimates derived through seamless integration of computer-aided design, meshing and computational fluid dynamics analysis tools i.e., computer aided 3-D interactive application, ICEM, and FLUENT. The effects of using low-fidelity and high-fidelity analyses are studied in depth using a small-scale (length nominally less than 400 mm) and light-weight (less than 450 g) toy submarine. Useful insights on possible means to identify appropriateness of fidelity models via correlation measures are proposed. The term optimality used in this paper refers to optimal hull form shapes that satisfy placement of a set of prescribed internal components.

Unmanned underwater vehicles (UUVs) are becoming an attractive option for maritime search and survey operations as they are cheap and efficient compared to conventional use of divers or manned submersibles. Consequently, there has been a growing interest in UUV research among scientific and engineering communities. Although UUVs have received significant research interest in recent years, limited attention has been paid towards design and development of mini/micro UUVs (usually less than 1 foot in length). Micro unmanned underwater vehicles (μUUVs) are particularly attractive for deployment in extraordinarily confined spaces such as inspection of intricate underwater structures, ship wrecks, oil pipe lines or extreme hazardous areas. This paper considers previous work done in the field of miniature UUVs and presents an optimization framework for preliminary design of that class of UUVs. A state-of-the-art optimization algorithm namely infeasibility driven evolutionary algorithm (IDEA) is used to carry out optimization of the μUUV designs. The framework is subsequently used to identify optimal design of a torpedo-shaped μUUV with an overall length of six inches (152.4 mm). The preliminary design identified through the process of optimization is further analyzed with the help of a computer-aided design tool to come up with a detailed design. The final design has since then been built and is currently undergoing trials.

This paper proposes an efficient, adaptive constraint handling approach that can be used within the class of evolutionary multi-objective optimization (EMO) algorithms. The proposed constraint handling approach is presented within the framework of one of the most successful algorithms i.e. multi-objective evolutionary algorithm based on decomposition (MOEA/D) [1]. The constraint handling mechanism adaptively decides on the violation threshold for comparison. The violation threshold is based on the type of constraints, size of the feasible space and the search outcome. Such a process intrinsically treats constraint violation and objective function values separately and adds a selection pressure, wherein infeasible solutions with violations less than the identified threshold are considered at par with feasible solutions. As illustrated, the constraint handling scheme extends the current capability of MOEA/D to deal with constraints. The performance of the algorithm is illustrated using 10 commonly studied benchmark problems and a real-world constraint optimization problem, and compared with the results obtained using yet another commonly used form i.e. Nondominated Sorting Genetic Algorithm (NSGA-II).