The video game industry is one of the most innovative, competitive, and rapidly growing industries. The industry's successes along with the increasing gamers’ expectations result in always larger and more complex games. These games thus must be developed with game engines, which have become correspondingly more sophisticated. Today, game engine developers find game engine development challenging. To support the process of creating and maintaining game engines, we propose COSA, an approach based on applying a consensus algorithm to a set of game engine architectures. Our approach generates a model that suggests the most commonly used subsystems in game-engine architectures, ranked by their degree of coupling. The model can be used by developers as a starting point when deciding what subsystems to include when building a game engine and points out the most coupled subsystems, which can play an important role towards higher subsystems' maintainability and reusability. We evaluate the approach by comparing the results of our approach against predefined ground truth data. The result of our approach matches the subsystems defined in the ground truth data and it shows that the most coupled subsystems are core, low-level rendering, third-party SDKs, and world editor. Additionally, when comparing game engine architectures, we observe that most architectures are composed of nearly the same set of subsystems. Our approach COSA thus helps game-engine developers fairly compare their engines and focus their attention on the ``important'' subsystems.
Agents for computer use (ACUs) are an emerging class of systems capable of executing complex tasks on digital devices - such as desktops, mobile phones, and web platforms - given instructions in natural language. These agents can automate tasks by controlling software via low-level actions like mouse clicks and touchscreen gestures. However, despite rapid progress, ACUs are not yet mature for everyday use. In this survey, we investigate the state-of-the-art, trends, and research gaps in the development of practical ACUs. We provide a comprehensive review of the ACU landscape, introducing a unifying taxonomy spanning three dimensions: (I) the domain perspective, characterizing agent operating contexts; (II) the interaction perspective, describing observation modalities (e.g., screenshots, HTML) and action modalities (e.g., mouse, keyboard, code execution); and (III) the agent perspective, detailing how agents perceive, reason, and learn. We review 87 ACUs and 33 datasets across foundation model-based and classical approaches through this taxonomy. Our analysis identifies six major research gaps: insufficient generalization, inefficient learning, limited planning, low task complexity in benchmarks, non-standardized evaluation, and a disconnect between research and practical conditions. To address these gaps, we advocate for: (a) vision-based observations and low-level control to enhance generalization; (b) adaptive learning beyond static prompting; (c) effective planning and reasoning methods and models; (d) benchmarks that reflect real-world task complexity; (e) standardized evaluation based on task success; (f) aligning agent design with real-world deployment constraints. Together, our taxonomy and analysis establish a foundation for advancing ACU research toward general-purpose agents for robust and scalable computer use.
The continuous growth of the mobile apps industry creates a competition among apps developers. To succeed, app developers must attract and retain users. User reviews provide a wealth of information about bugs to fix and features to add and can help app developers offer high-quality apps. However, apps may receive hundreds of unstructured reviews, which makes transforming them into change requests a difficult task. Approaches exist for analyzing and extracting topics from mobile app reviews, however, prioritizing these reviews has not gained much attention. In this study, we introduce the use of a consensus algorithm to help developers prioritize user reviews for the purpose of app evolution. We evaluate the usefulness of our approach and meaningfulness of its consensus rankings on four Android apps. We compare the rankings against reviews ranked by app developers manually and show that there is a strong correlation between the two (average Kendall rank correlation coefficient = 0.516). Thus, our approach can prioritize user reviews and help developers focus their time/effort on improving their apps instead of on identifying reviews to address in the next release.
Developers must complete change tasks on large software systems for maintenance and development purposes. Having a custom software system with numerous instances that meet the growing client demand for features and functionalities increases the software complexity and size. Developers, especially newcomers, must spend a significant amount of time navigating through the source code and switching back and forth between files in order to understand such a system and find the parts relevant for performing current tasks. This navigation can be difficult, time consuming and affect developers' productivity.
To help guide developers' navigation towards resolving tasks successfully with minimal time and effort, we present a task-based recommendation approach that exploits aggregated developers' interaction traces. Our novel approach, Consensus Task Interaction Trace Recommender (CITR), recommends file(s)-to-edit that help perform a set of tasks based on a tasks-related set of interaction traces obtained from developers who performed the same or similar change tasks on different custom instances of the same system. Our approach uses a consensus algorithm, which takes as input task-related interaction traces and recommends a consensus task interaction trace that developers can use to complete given similar change tasks that require editing common file(s).
To evaluate the efficiency of our approach, we perform three different evaluations. The first evaluation measures the recommendation accuracy of CITR results. In the second evaluation, we assess to what extent CITR can help developers by conducting an observational comparative experiment in which two groups of developers performed evaluation change tasks with and without the results of CITR. In the third and last evaluation, we compare CITR to a state-of-the-art recommendation approach, MI \citep{lee2014impact}. Results with statistical significance report that CITR can correctly recommend on average 72\% of the files to be edited. Furthermore, our evaluation demonstrates that CITR can increase developers’ successful task completion rate. CITR outperforms MI by an average of 40% higher recommendation accuracy.
Computer-Aided Engineering (CAE) enables simulation experts to optimize complex models, but faces challenges in user experience (UX) that limit efficiency and accessibility. While artificial intelligence (AI) has demonstrated potential to enhance CAE processes, research integrating these fields with a focus on UX remains fragmented. This paper presents a multivocal literature review (MLR) examining how AI enhances UX in CAE software across both academic research and industry implementations. Our analysis reveals significant gaps between academic explorations and industry applications, with companies actively implementing LLMs, adaptive UIs, and recommender systems while academic research focuses primarily on technical capabilities without UX validation. Key findings demonstrate opportunities in AI-powered guidance, adaptive interfaces, and workflow automation that remain underexplored in current research. By mapping the intersection of these domains, this study provides a foundation for future work to address the identified research gaps and advance the integration of AI to improve CAE user experience.