arXiv subjects

Ze Shi Li

Publications and source records attributed to Ze Shi Li.

At least 19 recordsLinked to original sources

arXiv2026

How Do Developers Interact with AI? An Exploratory Study on Modeling Developer Programming Behavior

Artificial Intelligence (AI) is reshaping how developers adopt software engineering practices, yet the multi-dimensional nature of developer-AI interaction remains under-explored. Prior studies have primarily examined dimensions observable from developer activities such as "Prompt Crafting" and "Code Editing," overlooking how hidden intentions and emotional dimensions intertwine with concrete actions during AI-assisted programming. To understand this phenomenon, we conducted a mixed-methods study with 76 developers split into AI-assisted and non-AI groups. Each performed programming tasks (Python with API management or Java with SQL). Developers retrospectively labeled their self-reported intentions, tool-supported actions, and emotions from screen recordings, supplemented by surveys and interviews. Our user study resulted in a novel model named S-IASE with four dimensions to describe programming behavior: intention, action, supporting tool, and emotion for a given development state. Our analysis reveals aggregated and sequential behavioral patterns. For example, using AI assistants often makes developers more focused on actively creating code, evaluating, and verifying generated results. AI-assisted participants showed emotionally stable development flow, as opposed to non-AI-assisted participants who experienced more fluctuating emotions. Interviews revealed further nuance: some developers reported impostor-like feelings, expressing guilt or self-doubt about relying on AI. Our work bridges an important gap in understanding the complexities of developer-AI interaction in programming context.

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arXiv2026

The Nature of Technical Debt in Research Software

Research software (also called scientific software) is essential for advancing scientific endeavours. Research software encapsulates complex algorithms and domain-specific knowledge and is a fundamental component of all science. A pervasive challenge in developing research software is technical debt, which can adversely affect reliability, maintainability, and scientific validity. Research software often relies on the initiative of the scientific community for maintenance, requiring diverse expertise in both scientific and software engineering domains. The extent and nature of technical debt in research software are little studied, in particular, what forms it takes, and what the science teams developing this software think about their technical debt. In this paper we describe our multi-method study examining technical debt in research software. We begin by examining instances of self-reported technical debt in research code, examining 28k code comments across nine research software projects. Then, building on our findings, we interview research software engineers and scientists about how this technical debt manifests itself in their experience, and what costs it has for research software and research outputs more generally. We identify nine types of self-admitted technical debt unique to research software, and four themes impacting this technical debt.

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arXiv2025

REConnect: Participatory RE for Social Sustainability

Context: Software increasingly shapes daily life, making requirements engineering (RE) essential for ensuring systems contribute to community social sustainability. Yet automated elicitation practices risk distancing RE from the cultural, social, and political contexts that inform user needs, systematically excluding the communities most dependent on socially impactful software. AI-assisted RE has intensified this trend. Objective: This paper introduces REConnect, a human-centered participatory RE framework that recenters requirements work on human connection and relationality as the foundation for understanding lived experiences and ensuring alignment with community values and aspirations. Methods: REConnect was derived through qualitative analysis of 26 community-engaged software projects conducted through the INSPIRE program at the University of Victoria between 2022 and 2025, spanning rural Nepal, urban Canada, and remote Arctic Canada. We conducted a reflective synthesis across all 26 projects, followed by in-depth thematic analysis of three illustrative projects. Results: Three core principles are articulated: building trusting relationships, co-creating with and alongside stakeholders, and empowering users as agents of change. Each is operationalized through actionable REConnect Actions (REActions) embedding relationality and continuous stakeholder engagement throughout the project lifecycle. Conclusions: REConnect positions human connection as the foundation of RE for socio-technical systems aiming toward social sustainability. While AI can accelerate certain RE activities, its integration must be governed by participatory principles that preserve human agency and ensure marginalized voices are not excluded. We discuss how REConnect integrates with AI support while maintaining critical human agency in requirements engineering.

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arXiv2025

Objectifying the Subjective: Cognitive Biases in Topic Interpretations

Interpretation of topics is crucial for their downstream applications. State-of-the-art evaluation measures of topic quality such as coherence and word intrusion do not measure how much a topic facilitates the exploration of a corpus. To design evaluation measures grounded on a task, and a population of users, we do user studies to understand how users interpret topics. We propose constructs of topic quality and ask users to assess them in the context of a topic and provide rationale behind evaluations. We use reflexive thematic analysis to identify themes of topic interpretations from rationales. Users interpret topics based on availability and representativeness heuristics rather than probability. We propose a theory of topic interpretation based on the anchoring-and-adjustment heuristic: users anchor on salient words and make semantic adjustments to arrive at an interpretation. Topic interpretation can be viewed as making a judgment under uncertainty by an ecologically rational user, and hence cognitive biases aware user models and evaluation frameworks are needed.

cs.CL↗

arXiv2025

QoSBERT: An Uncertainty-Aware Approach based on Pre-trained Language Models for Service Quality Prediction

Accurate prediction of Quality of Service (QoS) metrics is fundamental for selecting and managing cloud based services. Traditional QoS models rely on manual feature engineering and yield only point estimates, offering no insight into the confidence of their predictions. In this paper, we propose QoSBERT, the first framework that reformulates QoS prediction as a semantic regression task based on pre trained language models. Unlike previous approaches relying on sparse numerical features, QoSBERT automatically encodes user service metadata into natural language descriptions, enabling deep semantic understanding. Furthermore, we integrate a Monte Carlo Dropout based uncertainty estimation module, allowing for trustworthy and risk-aware service quality prediction, which is crucial yet underexplored in existing QoS models. QoSBERT applies attentive pooling over contextualized embeddings and a lightweight multilayer perceptron regressor, fine tuned jointly to minimize absolute error. We further exploit the resulting uncertainty estimates to select high quality training samples, improving robustness in low resource settings. On standard QoS benchmark datasets, QoSBERT achieves an average reduction of 11.7% in MAE and 6.7% in RMSE for response time prediction, and 6.9% in MAE for throughput prediction compared to the strongest baselines, while providing well calibrated confidence intervals for robust and trustworthy service quality estimation. Our approach not only advances the accuracy of service quality prediction but also delivers reliable uncertainty quantification, paving the way for more trustworthy, data driven service selection and optimization.

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arXiv2024

Unveiling Inclusiveness-Related User Feedback in Mobile Applications

In an era of rapidly expanding software usage, catering to the diverse needs of users from various backgrounds has become a critical challenge. Inclusiveness, representing a core human value, is frequently overlooked during software development, leading to user dissatisfaction. Users often engage in discourse on online platforms where they indicate their concerns. In this study, we leverage user feedback from three popular online sources Reddit, Google Play Store, and X, for 50 of the most popular apps in the world. Using a Socio-Technical Grounded Theory approach, we analyzed 22,000 posts across the three sources. We organize our empirical results in a taxonomy for inclusiveness comprising 5 major categories: Algorithmic Bias, Technology, Demography, Accessibility, and Other Human Values. To explore automated support for identifying inclusiveness-related posts, we experimented with a large language model (GPT4o-mini) and found that it is capable of identifying inclusiveness-related user feedback. We provide implications and recommendations that can help software practitioners to better identify inclusiveness issues to support a wider range of users

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arXiv2024

Feature Noise Resilient for QoS Prediction with Probabilistic Deep Supervision

Accurate Quality of Service (QoS) prediction is essential for enhancing user satisfaction in web recommendation systems, yet existing prediction models often overlook feature noise, focusing predominantly on label noise. In this paper, we present the Probabilistic Deep Supervision Network (PDS-Net), a robust framework designed to effectively identify and mitigate feature noise, thereby improving QoS prediction accuracy. PDS-Net operates with a dual-branch architecture: the main branch utilizes a decoder network to learn a Gaussian-based prior distribution from known features, while the second branch derives a posterior distribution based on true labels. A key innovation of PDS-Net is its condition-based noise recognition loss function, which enables precise identification of noisy features in objects (users or services). Once noisy features are identified, PDS-Net refines the feature's prior distribution, aligning it with the posterior distribution, and propagates this adjusted distribution to intermediate layers, effectively reducing noise interference. Extensive experiments conducted on two real-world QoS datasets demonstrate that PDS-Net consistently outperforms existing models, achieving an average improvement of 8.91% in MAE on Dataset D1 and 8.32% on Dataset D2 compared to the ate-of-the-art. These results highlight PDS-Net's ability to accurately capture complex user-service relationships and handle feature noise, underscoring its robustness and versatility across diverse QoS prediction environments.

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arXiv2024

Dual Latent State Learning: Exploiting Regional Network Similarities for QoS Prediction

Individual objects, whether users or services, within a specific region often exhibit similar network states due to their shared origin from the same city or autonomous system (AS). Despite this regional network similarity, many existing techniques overlook its potential, resulting in subpar performance arising from challenges such as data sparsity and label imbalance. In this paper, we introduce the regional-based dual latent state learning network(R2SL), a novel deep learning framework designed to overcome the pitfalls of traditional individual object-based prediction techniques in Quality of Service (QoS) prediction. Unlike its predecessors, R2SL captures the nuances of regional network behavior by deriving two distinct regional network latent states: the city-network latent state and the AS-network latent state. These states are constructed utilizing aggregated data from common regions rather than individual object data. Furthermore, R2SL adopts an enhanced Huber loss function that adjusts its linear loss component, providing a remedy for prevalent label imbalance issues. To cap off the prediction process, a multi-scale perception network is leveraged to interpret the integrated feature map, a fusion of regional network latent features and other pertinent information, ultimately accomplishing the QoS prediction. Through rigorous testing on real-world QoS datasets, R2SL demonstrates superior performance compared to prevailing state-of-the-art methods. Our R2SL approach ushers in an innovative avenue for precise QoS predictions by fully harnessing the regional network similarities inherent in objects.

cs.LG↗

arXiv2024

Software Engineering Through Community-Engaged Learning and an Inclusive Network

Retaining diverse, underrepresented students in computer science and software engineering programs is a significant concern for universities. In this chapter, we describe the INSPIRE: STEM for Social Impact program at the University of Victoria, Canada, which leverages the three principles of self-determination theory competence, relatedness, and autonomy in the design of strategies to empower women and other underrepresented groups in using software and other engineering solutions to approach sustainability, community-driven problems. We also describe lessons learned from a first successful year that involved over 30 students, 6 community partners (sustainability problem owners), and over 20 industry and academic mentors and reached out to more than 200 solution end users in our communities. Finally, we provide recommendations for universities and organizations who may want to adopt our approach. In the program 24 diverse students (in terms of gender, sexual orientation, ethnicity, academic standing, and background) divided into six teams paired with six community partners worked on solving society impactful problems and developed solutions for a number of respective community partners. Each team was supported by an experienced upper year student and mentors from industry and community throughout the program. The experiential learning approach of the program allowed the students to learn a variety of soft and technical skills while developing a solution that has a social and/or environmental impact. Having a diverse team and creating a solution for real end users motivated the students to actively collaborate with their peers, community partners, and mentors resulting in the development of an inclusive network. A network of like minded people is crucial in empowering underrepresented individuals and inspiring them to remain in the computer science and software engineering fields.

cs.CY↗

arXiv2024

AI Tool Use and Adoption in Software Development by Individuals and Organizations: A Grounded Theory Study

AI assistance tools such as ChatGPT, Copilot, and Gemini have dramatically impacted the nature of software development in recent years. Numerous studies have studied the positive benefits that practitioners have achieved from using these tools in their work. While there is a growing body of knowledge regarding the usability aspects of leveraging AI tools, we still lack concrete details on the issues that organizations and practitioners need to consider should they want to explore increasing adoption or use of AI tools. In this study, we conducted a mixed methods study involving interviews with 26 industry practitioners and 395 survey respondents. We found that there are several motives and challenges that impact individuals and organizations and developed a theory of AI Tool Adoption. For example, we found creating a culture of sharing of AI best practices and tips as a key motive for practitioners' adopting and using AI tools. In total, we identified 2 individual motives, 4 individual challenges, 3 organizational motives, and 3 organizational challenges, and 3 interleaved relationships. The 3 interleaved relationships act in a push-pull manner where motives pull practitioners to increase the use of AI tools and challenges push practitioners away from using AI tools.

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arXiv2024

Industry Practitioners Perspectives on AI Model Quality: Perceptions, Challenges, and Solutions

Artificial Intelligence (AI) is now used across nearly every industry, making AI model quality essential for building reliable and trustworthy systems. Historically, correctness has been the main focus, but industry AI models must also satisfy many other important quality attributes. To understand how these attributes are perceived, the challenges they create, and the solutions used in practice, we identify nine key quality attributes and interview 15 AI practitioners from diverse backgrounds. The interviews show that practitioners prioritize attributes differently depending on context. For example, efficiency can matter more than correctness in real-time applications, while scalability and deployability are no longer seen as primary concerns. Data imbalance emerges as a major obstacle to maintaining model correctness and robustness, and practitioners commonly use mitigation strategies such as active learning. We validate our main findings with a survey of 50 practitioners, which shows that most of the findings are widely recognized. These results can help researchers focus on the attributes practitioners value most and avoid improving one attribute at the expense of others that are considered more critical.

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arXiv2023

Unveiling the Life Cycle of User Feedback: Best Practices from Software Practitioners

User feedback has grown in importance for organizations to improve software products. Prior studies focused primarily on feedback collection and reported a high-level overview of the processes, often overlooking how practitioners reason about, and act upon this feedback through a structured set of activities. In this work, we conducted an exploratory interview study with 40 practitioners from 32 organizations of various sizes and in several domains such as e-commerce, analytics, and gaming. Our findings indicate that organizations leverage many different user feedback sources. Social media emerged as a key category of feedback that is increasingly critical for many organizations. We found that organizations actively engage in a number of non-trivial activities to curate and act on user feedback, depending on its source. We synthesize these activities into a life cycle of managing user feedback. We also report on the best practices for managing user feedback that we distilled from responses of practitioners who felt that their organization effectively understood and addressed their users' feedback. We present actionable empirical results that organizations can leverage to increase their understanding of user perception and behavior for better products thus reducing user attrition.

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arXiv2023

A Data-Driven Approach for Finding Requirements Relevant Feedback from TikTok and YouTube

The increasing importance of videos as a medium for engagement, communication, and content creation makes them critical for organizations to consider for user feedback. However, sifting through vast amounts of video content on social media platforms to extract requirements-relevant feedback is challenging. This study delves into the potential of TikTok and YouTube, two widely used social media platforms that focus on video content, in identifying relevant user feedback that may be further refined into requirements using subsequent requirement generation steps. We evaluated the prospect of videos as a source of user feedback by analyzing audio and visual text, and metadata (i.e., description/title) from 6276 videos of 20 popular products across various industries. We employed state-of-the-art deep learning transformer-based models, and classified 3097 videos consisting of requirements relevant information. We then clustered relevant videos and found multiple requirements relevant feedback themes for each of the 20 products. This feedback can later be refined into requirements artifacts. We found that product ratings (feature, design, performance), bug reports, and usage tutorial are persistent themes from the videos. Video-based social media such as TikTok and YouTube can provide valuable user insights, making them a powerful and novel resource for companies to improve customer-centric development.

cs.HC↗

arXiv2023

"Software is the easy part of Software Engineering" -- Lessons and Experiences from A Large-Scale, Multi-Team Capstone Course

Capstone courses in undergraduate software engineering are a critical final milestone for students. These courses allow students to create a software solution and demonstrate the knowledge they accumulated in their degrees. However, a typical capstone project team is small containing no more than 5 students and function independently from other teams. To better reflect real-world software development and meet industry demands, we introduce in this paper our novel capstone course. Each student was assigned to a large-scale, multi-team (i.e., company) of up to 20 students to collaboratively build software. Students placed in a company gained first-hand experiences with respect to multi-team coordination, integration, communication, agile, and teamwork to build a microservices based project. Furthermore, each company was required to implement plug-and-play so that their services would be compatible with another company, thereby sharing common APIs. Through developing the product in autonomous sub-teams, the students enhanced not only their technical abilities but also their soft skills such as communication and coordination. More importantly, experiencing the challenges that arose from the multi-team project trained students to realize the pitfalls and advantages of organizational culture. Among many lessons learned from this course experience, students learned the critical importance of building team trust. We provide detailed information about our course structure, lessons learned, and propose recommendations for other universities and programs. Our work concerns educators interested in launching similar capstone projects so that students in other institutions can reap the benefits of large-scale, multi-team development

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arXiv2022

Narratives: the Unforeseen Influencer of Privacy Concerns

Privacy requirements are increasingly growing in importance as new privacy regulations are enacted. To adequately manage privacy requirements, organizations not only need to comply with privacy regulations, but also consider user privacy concerns. In this exploratory study, we used Reddit as a source to understand users' privacy concerns regarding software applications. We collected 4.5 million posts from Reddit and classified 129075 privacy related posts, which is a non-negligible number of privacy discussions. Next, we clustered these posts and identified 9 main areas of privacy concerns. We use the concept of narratives from economics (i.e., posts that can go viral) to explain the phenomenon of what and when users change in their discussion of privacy. We further found that privacy discussions change over time and privacy regulatory events have a short term impact on such discussions. However, narratives have a notable impact on what and when users discussed about privacy. Considering narratives could guide software organizations in eliciting the relevant privacy concerns before developing them as privacy requirements.

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arXiv2021

Continuously Managing NFRs: Opportunities and Challenges in Practice

Non-functional requirements (NFR), which include performance, availability, and maintainability, are vitally important to overall software quality. However, research has shown NFRs are, in practice, poorly defined and difficult to verify. Continuous software engineering practices, which extend agile practices, emphasize fast paced, automated, and rapid release of software that poses additional challenges to handling NFRs. In this multi-case study we empirically investigated how three organizations, for which NFRs are paramount to their business survival, manage NFRs in their continuous practices. We describe four practices these companies use to manage NFRs, such as offloading NFRs to cloud providers or the use of metrics and continuous monitoring, both of which enable almost real-time feedback on managing the NFRs. However, managing NFRs comes at a cost as we also identified a number of challenges these organizations face while managing NFRs in their continuous software engineering practices. For example, the organizations in our study were able to realize an NFR by strategically and heavily investing in configuration management and infrastructure as code, in order to offload the responsibility of NFRs; however, this offloading implied potential loss of control. Our discussion and key research implications show the opportunities, trade-offs, and importance of the unique give-and-take relationship between continuous software engineering and NFRs. Research artifacts may be found at https://doi.org/10.5281/zenodo.3376342.

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arXiv2021

Uncovering the Benefits and Challenges of Continuous Integration Practices

In 2006, Fowler and Foemmel defined ten core Continuous Integration (CI) practices that could increase the speed of software development feedback cycles and improve software quality. Since then, these practices have been widely adopted by industry and subsequent research has shown they improve software quality. However, there is poor understanding of how organizations implement these practices, of the benefits developers perceive they bring, and of the challenges developers and organizations experience in implementing them. In this paper, we discuss a multiple-case study of three small- to medium-sized companies using the recommended suite of ten CI practices. Using interviews and activity log mining, we learned that these practices are broadly implemented but how they are implemented varies depending on their perceived benefits, the context of the project, and the CI tools used by the organization. We also discovered that CI practices can create new constraints on the software process that hurt feedback cycle time. For researchers, we show that how CI is implemented varies, and thus studying CI (for example, using data mining) requires understanding these differences as important context for research studies. For practitioners, our findings reveal in-depth insights on the possible benefits and challenges from using the ten practices, and how project context matters.

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arXiv2020

The Lack of Shared Understanding of Non-Functional Requirements in Continuous Software Engineering: Accidental or Essential?

Building shared understanding of requirements is key to ensuring downstream software activities are efficient and effective. However, in continuous software engineering (CSE) some lack of shared understanding is an expected, and essential, part of a rapid feedback learning cycle. At the same time, there is a key trade-off with avoidable costs, such as rework, that come from accidental gaps in shared understanding. This trade-off is even more challenging for non-functional requirements (NFRs), which have significant implications for product success. Comprehending and managing NFRs is especially difficult in small, agile organizations. How such organizations manage shared understanding of NFRs in CSE is understudied. We conducted a case study of three small organizations scaling up CSE to further understand and identify factors that contribute to lack of shared understanding of NFRs, and its relationship to rework. Our in-depth analysis identified 41 NFR-related software tasks as rework due to a lack of shared understanding of NFRs. Of these 41 tasks 78% were due to avoidable (accidental) lack of shared understanding of NFRs. Using a mixed-methods approach we identify factors that contribute to lack of shared understanding of NFRs, such as the lack of domain knowledge, rapid pace of change, and cross-organizational communication problems. We also identify recommended strategies to mitigate lack of shared understanding through more effective management of requirements knowledge in such organizations. We conclude by discussing the complex relationship between shared understanding of requirements, rework and, CSE.

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