arXiv subjects

Maleknaz Nayebi

Publications and source records attributed to Maleknaz Nayebi.

At least 19 recordsLinked to original sources

arXiv2025

GitHub Marketplace: Driving Automation and Fostering Innovation in Software Development

GitHub, a central hub for collaborative software development, has revolutionized the open-source software (OSS) ecosystem through its GitHub Marketplace, a platform launched in 2017 to host automation tools aimed at enhancing the efficiency and scalability of software projects. As the adoption of automation in OSS production grows, understanding the trends, characteristics, and underlying dynamics of this marketplace has become vital. Furthermore, despite the rich repository of academic research on software automation, a disconnect persists between academia and industry practices. This study seeks to bridge this gap by providing a systematic analysis of the GitHub Marketplace, comparing trends observed in industry tools with advancements reported in academic literature, and identifying areas where academia can contribute to practical innovation.

cs.SE↗

arXiv2025

Extension Decisions in Open Source Software Ecosystem

GitHub Marketplace is expanding by approximately 41% annually, with new tools; however, many additions replicate existing functionality. We study this phenomenon in the platform's largest segment, Continuous Integration (CI), by linking 6,983 CI Actions to 3,869 providers and mining their version histories. Our graph model timestamps every functionality's debut, tracks its adoption, and clusters redundant tools. We find that approximately 65% of new CI Actions replicate existing capabilities, typically within six months, and that a small set of first-mover Actions accounts for most subsequent forks and extensions. These insights enable developers to choose the optimal moment to launch, target unmet functionality, and help maintainers eliminate redundant tools. We publish the complete graph and dataset to encourage longitudinal research on innovation and competition in software ecosystems, and to provide practitioners with a data-driven roadmap for identifying emerging trends and guiding product strategy.

cs.SE↗

arXiv2025

The Impact of Foundational Models on Patient-Centric e-Health Systems

As Artificial Intelligence (AI) becomes increasingly embedded in healthcare technologies, understanding the maturity of AI in patient-centric applications is critical for evaluating its trustworthiness, transparency, and real-world impact. In this study, we investigate the integration and maturity of AI feature integration in 116 patient-centric healthcare applications. Using Large Language Models (LLMs), we extracted key functional features, which are then categorized into different stages of the Gartner AI maturity model. Our results show that over 86.21\% of applications remain at the early stages of AI integration, while only 13.79% demonstrate advanced AI integration.

cs.AI↗

arXiv2025

ImageR: Enhancing Bug Report Clarity by Screenshots

In issue-tracking systems, incorporating screenshots significantly enhances the clarity of bug reports, facilitating more efficient communication and expediting issue resolution. However, determining when and what type of visual content to include remains challenging, as not all attachments effectively contribute to problem-solving; studies indicate that 22.5% of images in issue reports fail to aid in resolving the reported issues. To address this, we introduce ImageR, an AI model and tool that analyzes issue reports to assess the potential benefits of including screenshots and recommends the most pertinent types when appropriate. By proactively suggesting relevant visuals, ImageR aims to make issue reports clearer, more informative, and time-efficient. We have curated and publicly shared a dataset comprising 6,235 Bugzilla issues, each meticulously labeled with the type of image attachment, providing a valuable resource for benchmarking and advancing research in image processing within developer communication contexts. To evaluate ImageR, we conducted empirical experiments on a subset of these reports from various Mozilla projects. The tool achieved an F1-score of 0.76 in determining when images are needed, with 75% of users finding its recommendations highly valuable. By minimizing the back-and-forth communication often needed to obtain suitable screenshots, ImageR streamlines the bug reporting process. Furthermore, it guides users in selecting the most effective visual documentation from ten established categories, potentially reducing resolution times and improving the quality of bug documentation. ImageR is open-source, inviting further use and improvement by the community. The labeled dataset offers a rare resource for benchmarking and exploring image processing in the context of developer communication.

cs.SE↗

arXiv2025

One Documentation Does Not Fit All: Case Study of TensorFlow Documentation

Software documentation guides the proper use of tools or services. With the rapid growth of machine learning libraries, individuals from various fields are incorporating machine learning into their workflows through programming. However, many of these users lack software engineering experience, affecting the usability of the documentation. Traditionally, software developers have created documentation primarily for their peers, making it challenging for others to interpret and effectively use these resources. Moreover, no study has specifically focused on machine learning software documentation or analyzing the backgrounds of developers who rely on such documentation, highlighting a critical gap in understanding how to make these resources more accessible. This study examined customization trends in TensorFlow tutorials and compared these artifacts to analyze content and design differences. We also analyzed Stack Overflow questions related to TensorFlow documentation to understand the types of questions and the backgrounds of the developers asking them. Further, we developed two taxonomies based on the nature and triggers of the questions for machine learning software. Our findings showed no significant differences in the content or the nature of the questions across different tutorials. Our results show that 24.9% of the questions concern errors and exceptions, while 64.3% relate to inadequate and non-generalizable examples in the documentation. Despite efforts to create customized documentation, our analysis indicates that current TensorFlow documentation does not effectively support its target users.

cs.SE↗

arXiv2025

Inferring Questions from Programming Screenshots

The integration of generative AI into developer forums like Stack Overflow presents an opportunity to enhance problem-solving by allowing users to post screenshots of code or Integrated Development Environments (IDEs) instead of traditional text-based queries. This study evaluates the effectiveness of various large language models (LLMs), specifically LLAMA, GEMINI, and GPT-4o in interpreting such visual inputs. We employ prompt engineering techniques, including in-context learning, chain-of-thought prompting, and few-shot learning, to assess each model's responsiveness and accuracy. Our findings show that while GPT-4o shows promising capabilities, achieving over 60% similarity to baseline questions for 51.75% of the tested images, challenges remain in obtaining consistent and accurate interpretations for more complex images. This research advances our understanding of the feasibility of using generative AI for image-centric problem-solving in developer communities, highlighting both the potential benefits and current limitations of this approach while envisioning a future where visual-based debugging copilot tools become a reality.

cs.SE↗

arXiv2025

Prompt Engineering or Fine-Tuning: An Empirical Assessment of LLMs for Code

The rapid advancements in large language models (LLMs) have greatly expanded the potential for automated code-related tasks. Two primary methodologies are used in this domain: prompt engineering and fine-tuning. Prompt engineering involves applying different strategies to query LLMs, like ChatGPT, while fine-tuning further adapts pre-trained models, such as CodeBERT, by training them on task-specific data. Despite the growth in the area, there remains a lack of comprehensive comparative analysis between the approaches for code models. In this paper, we evaluate GPT-4 using three prompt engineering strategies -- basic prompting, in-context learning, and task-specific prompting -- and compare it against 17 fine-tuned models across three code-related tasks: code summarization, generation, and translation. Our results indicate that GPT-4 with prompt engineering does not consistently outperform fine-tuned models. For instance, in code generation, GPT-4 is outperformed by fine-tuned models by 28.3% points on the MBPP dataset. It also shows mixed results for code translation tasks. Additionally, a user study was conducted involving 27 graduate students and 10 industry practitioners. The study revealed that GPT-4 with conversational prompts, incorporating human feedback during interaction, significantly improved performance compared to automated prompting. Participants often provided explicit instructions or added context during these interactions. These findings suggest that GPT-4 with conversational prompting holds significant promise for automated code-related tasks, whereas fully automated prompt engineering without human involvement still requires further investigation.

cs.SE↗

arXiv2024

Recommending and Release Planning of User-Driven Functionality Deletion for Mobile Apps

Evolving software with an increasing number of features poses challenges in terms of comprehensibility and usability. Traditional software release planning has predominantly focused on orchestrating the addition of features, contributing to the growing complexity and maintenance demands of larger software systems. In mobile apps, an excess of functionality can significantly impact usability, maintainability, and resource consumption, necessitating a nuanced understanding of the applicability of the law of continuous growth to mobile apps. Previous work showed that the deletion of functionality is common and sometimes driven by user reviews. For most users, the removal of features is associated with negative sentiments, prompts changes in usage patterns, and may even result in user churn. Motivated by these preliminary results, we propose Radiation to input user reviews and recommend if any functionality should be deleted from an app's User Interface (UI). We evaluate radiation using historical data and survey developers' opinions. From the analysis of 190,062 reviews from 115 randomly selected apps, we show that Radiation can recommend functionality deletion with an average F-Score of 74% and if sufficiently many negative user reviews suggest so. We conducted a survey involving 141 software developers to gain insights into the decision-making process and the level of planning for feature deletions. Our findings indicate that 77.3% of the participants often or always plan for such deletions. This underscores the importance of incorporating feature deletion planning into the overall release decision-making process.

cs.SE↗

arXiv2024

Negative Results of Image Processing for Identifying Duplicate Questions on Stack Overflow

In the rapidly evolving landscape of developer communities, Q&A platforms serve as crucial resources for crowdsourcing developers' knowledge. A notable trend is the increasing use of images to convey complex queries more effectively. However, the current state-of-the-art method of duplicate question detection has not kept pace with this shift, which predominantly concentrates on text-based analysis. Inspired by advancements in image processing and numerous studies in software engineering illustrating the promising future of image-based communication on social coding platforms, we delved into image-based techniques for identifying duplicate questions on Stack Overflow. When focusing solely on text analysis of Stack Overflow questions and omitting the use of images, our automated models overlook a significant aspect of the question. Previous research has demonstrated the complementary nature of images to text. To address this, we implemented two methods of image analysis: first, integrating the text from images into the question text, and second, evaluating the images based on their visual content using image captions. After a rigorous evaluation of our model, it became evident that the efficiency improvements achieved were relatively modest, approximately an average of 1%. This marginal enhancement falls short of what could be deemed a substantial impact. As an encouraging aspect, our work lays the foundation for easy replication and hypothesis validation, allowing future research to build upon our approach.

cs.SE↗

arXiv2024

GitHub Marketplace for Automation and Innovation in Software Production

GitHub, renowned for facilitating collaborative code version control and software production in software teams, expanded its services in 2017 by introducing GitHub Marketplace. This online platform hosts automation tools to assist developers with the production of their GitHub-hosted projects, and it has become a valuable source of information on the tools used in the Open Source Software (OSS) community. In this exploratory study, we introduce GitHub Marketplace as a software marketplace by comprehensively exploring the platform's characteristics, features, and policies and identifying common themes in production automation. Further, we explore popular tools among practitioners and researchers and highlight disparities in the approach to these tools between industry and academia. We adopted the conceptual framework of software app stores from previous studies to examine 8,318 automated production tools (440 Apps and 7,878 Actions) across 32 categories on GitHub Marketplace. We explored and described the policies of this marketplace as a unique platform where developers share production tools for the use of other developers. Furthermore, we systematically mapped 515 research papers published from 2000 to 2021 and compared open-source academic production tools with those available in the marketplace. We found that although some of the automation topics in literature are widely used in practice, they have yet to align with the state of practice for automated production. We discovered that practitioners often use automation tools for tasks like "Continuous Integration" and "Utilities," while researchers tend to focus more on "Code Quality" and "Testing". Our study illuminates the landscape of open-source tools for automation production in industry and research.

cs.SE↗

arXiv2024

Examining Ownership Models in Software Teams: A Systematic Literature Review and a Replication Study

Effective ownership of software artifacts, particularly code, is crucial for accountability, knowledge sharing, and code quality enhancement. Researchers have proposed models linking ownership of software artifacts with developer performance and code quality. Our study aims to systematically examine various ownership models and provide a structured literature overview. Conducting a systematic literature review, we identified 79 relevant papers published between 2005 and 2022. We developed a taxonomy of ownership artifacts based on type, owners, and degree of ownership, along with compiling modeling variables and analytics types used in each study. Additionally, we assessed the replication status of each study. As a result, we identified nine distinct software artifacts whose ownership has been discussed in the literature, with "Code" being the most frequently analyzed artifact. We found that only three papers (3.79%) provided code and data, whereas nine papers (11.4%) provided only data. Using our systematic literature review results, we replicated experiments on nine priority projects at \texttt{Brightsquid}. The company aimed to compare its code quality against ownership factors in other teams, so we conducted a replication study using their data. Unlike prior studies, we found no strong correlation between minor contributors and bug numbers. Surprisingly, we found no strong link between the total number of developers modifying a file and bug counts, contrasting previous findings. However, we observed a significant correlation between major contributors and bug counts, diverging from earlier research.

cs.SE↗

arXiv2024

More Insight from Being More Focused: Analysis of Clustered Market Apps

The increasing attraction of mobile apps has inspired researchers to analyze apps from different perspectives. As with any software product, apps have different attributes such as size, content maturity, rating, category, or number of downloads. Current research studies mostly consider sampling across all apps. This often results in comparisons of apps being quite different in nature and category (games compared with weather and calendar apps), also being different in size and complexity. Similar to proprietary software and web-based services, more specific results can be expected from looking at more homogeneous samples as they can be received as a result of applying clustering. In this paper, we target homogeneous samples of apps to increase the degree of insight gained from analytics. As a proof-of-concept, we applied the clustering technique DBSCAN and subsequent correlation analysis between app attributes for a set of 940 open-source mobile apps from F-Droid. We showed that (i) clusters of apps with similar characteristics provided more insight compared to applying the same to the whole data and (ii) defining the similarity of apps based on the similarity of topics as created from the topic modeling technique Latent Dirichlet Allocation does not significantly improve clustering results.

cs.SE↗

arXiv2024

Analysis of Marketed versus Not-marketed Mobile App Releases

Market and user characteristics of mobile apps make their release management different from proprietary software products and web services. Despite the wealth of information regarding users' feedback on an app, an in-depth analysis of app releases is difficult due to the inconsistency and uncertainty of the information. To better understand and potentially improve app release processes, we analyze major, minor, and patch releases for releases following semantic versioning. In particular, we were interested in finding out the difference between marketed and not-marketed releases. Our results show that, in general, major, minor, and patch releases have significant differences in the release cycle duration, nature, and change velocity. We also observed that there is a significant difference between marketed and non-marketed mobile app releases in terms of cycle duration, nature and the extent of changes, and the number of opened and closed issues.

cs.SE↗

arXiv2023

Ownership in the Hands of Accountability at Brightsquid -- A Case Study and a Developer Survey

The COVID-19 pandemic has accelerated the adoption of digital health solutions. This has presented significant challenges for software development teams to swiftly adjust to the market need and demand. To address these challenges, product management teams have had to adapt their approach to software development, reshaping their processes to meet the demands of the pandemic. Brighsquid implemented a new task assignment process aimed at enhancing developer accountability toward the customer. To assess the impact of this change on code ownership, we conducted a code change analysis. Additionally, we surveyed 67 developers to investigate the relationship between accountability and ownership more broadly. The findings of our case study indicate that the revised assignment model not only increased the perceived sense of accountability within the production team but also improved code resilience against ownership changes. Moreover, the survey results revealed that a majority of the participating developers (67.5%) associated perceived accountability with artifact ownership.

cs.SE↗

arXiv2023

Mining Reddit Data to Elicit Students' Requirements During COVID-19 Pandemic

Data-driven requirements engineering leverages the abundance of openly accessible and crowdsourced information on the web. By incorporating user feedback provided about a software product, such as reviews in mobile app stores, these approaches facilitate the identification of issues, bug fixes, and implementation of change requests. However, relying solely on user feedback about a software product limits the possibility of eliciting all requirements, as users may not always have a clear understanding of their exact needs from the software, despite their wealth of experience with the problem, event, or challenges they encounter and use the software to assist them. In this study, we propose a shift in requirements elicitation, focusing on gathering feedback related to the problem itself rather than relying solely on feedback about the software product. We conducted a case study on student requirements during the COVID-19 pandemic in a higher education institution. We gathered their communications from Reddit during the pandemic and employed multiple machine-learning and natural language processing techniques to identify requirement sentences. We achieved the F-score of 0.79 using Naive Bayes with TF-IDF when benchmarking multiple techniques. The results lead us to believe that mining requirements from communication about a problem are feasible. While we present the preliminary results, we envision a future where these requirements complement conventionally elicited requirements and help to close the requirements gap.

cs.SE↗

arXiv2023

Image-based Communication on Social Coding Platforms

Visual content in the form of images and videos has taken over general-purpose social networks in a variety of ways, streamlining and enriching online communications. We are interested to understand if and to what extent the use of images is popular and helpful in social coding platforms. We mined nine years of data from two popular software developers' platforms: the Mozilla issue tracking system, i.e., Bugzilla, and the most well-known platform for developers' Q/A, i.e., Stack Overflow. We further triangulated and extended our mining results by performing a survey with 168 software developers. We observed that, between 2013 and 2022, the number of posts containing image data on Bugzilla and Stack Overflow doubled. Furthermore, we found that sharing images makes other developers engage more and faster with the content. In the majority of cases in which an image is included in a developer's post, the information in that image is complementary to the text provided. Finally, our results showed that when an image is shared, understanding the content without the information in the image is unlikely for 86.9\% of the cases. Based on these observations, we discuss the importance of considering visual content when analyzing developers and designing automation tools.

cs.SE↗

arXiv2023

User Driven Functionality Deletion for Mobile Apps

Evolving software with an increasing number of features is harder to understand and thus harder to use. Software release planning has been concerned with planning these additions. Moreover, software of increasing size takes more effort to be maintained. In the domain of mobile apps, too much functionality can easily impact usability, maintainability, and resource consumption. Hence, it is important to understand the extent to which the law of continuous growth applies to mobile apps. Previous work showed that the deletion of functionality is common and sometimes driven by user reviews. However, it is not known if these deletions are visible or important to the app users. In this study, we performed a survey study with 297 mobile app users to understand the significance of functionality deletion for them. Our results showed that for the majority of users, the deletion of features corresponds with negative sentiments and change in usage and even churn. Motivated by these preliminary results, we propose RADIATION to input user reviews and recommend if any functionality should be deleted from an app's User Interface (UI). We evaluate RADIATION using historical data and surveying developers' opinions. From the analysis of 190,062 reviews from 115 randomly selected apps, we show that RADIATION can recommend functionality deletion with an average F-Score of 74% and if sufficiently many negative user reviews suggest so.

cs.SE↗

arXiv2023

Developers' Perception of GitHub Actions: A Survey Analysis

GitHub introduced "Actions" in 2019 to increase workflow velocity and add customized automation to the repositories. Any individual can develop Actions for automating workflow on GitHub repositories and others can reuse them whenever open source. GitHub introduced its marketplace for commercializing and sharing these automation tools, which currently hosts 16,730 Actions. Yet, there are numerous Actions that are developed and distributed in local repositories and outside the Marketplace. So far, the research community conducted mining studies to understand Actions with a significant focus on CI/CD. We conducted a survey study with 90 Action developers and users to understand the motivations and best practices in using, developing, and debugging Actions, and the challenges associated with these tasks. We found that developers prefer Actions with verified creators and more stars when choosing between similar Actions, and often switch to an alternative Action when facing bugs or a lack of documentation. 60.87% of the developers consider the composition of YAML files, which are essential for Action integration, challenging and error-prone. They mainly check Q\&A forums to fix issues with these YAML files. Finally, developers tend to avoid using Actions (and hence automation) to reduce complexity, and security risk, or when the benefits of Actions are not worth the cost/effort of setting up Actions for automation.

cs.SE↗