7 citations · 22 across the 25 of their papers we have counts for
27 papers
The EPIC Framework for Spec-Driven Development
Bruno Claudino, Ruizhe Xing, Yuanhao Zuo +4
One practitioner we interviewed said their team writes "must" instead of "should" when instructing a coding agent, because the agent may treat "should" as optional. Small wording c…
''You Can't Open an LLM With a Screwdriver'': The De-Democratization of Software
Zixuan Feng, Italo Santos, Kostadin Damevski +1
Claims that generative AI will soon write all of the code have led to predictions that programming is nearing its end. In this vision paper, we argue against this assumption that b…
A Low-Cost Human-in-the-Loop Investigation of Toxicity on GitHub at Scale
Rahat Rizvi Rahman, Mia Mohammad Imran, Kostadin Damevski
Toxic interactions in open source discussions can alienate contributors and threaten project sustainability, yet prior empirical studies of GitHub toxicity have been limited in sca…
Investigating Conversational Agents to Support Secondary School Students Learning CSP
Matthew Frazier, Kostadin Damevski, Lori Pollock
Secondary school students enrolled in the AP Computer Science Principles (CSP) course commonly utilize web resources (e.g., tutorials, Q\&A sites) to better understand key concepts…
Towards Personalizing Secure Programming Education with LLM-Injected Vulnerabilities
Matthew Frazier, Kostadin Damevski
According to constructivist theory, students learn software security more effectively when examples are grounded in their own code. Generic examples often fail to connect with stud…
AXE: Grey-Box Exploitability Confirmation for Localized Vulnerability Reports
Amirali Sajadi, Tu Nguyen, Kostadin Damevski +1
Vulnerability detection tools are widely adopted in software projects, yet they often overwhelm maintainers with false positives and non-actionable reports. Automated exploitation…
A Survey on Large Language Model Impact on Software Evolvability and Maintainability: the Good, the Bad, the Ugly, and the Remedy
Bruno Claudino Matias, Savio Freire, Juliana Freitas +3
Context. Large Language Models (LLMs) are increasingly embedded in software engineering workflows for tasks including code generation, summarization, repair, and testing. Empirical…
Toxicity Ahead: Forecasting Conversational Derailment on GitHub
Mia Mohammad Imran, Robert Zita, Rahat Rizvi Rahman +2
Toxic interactions in Open Source Software (OSS) communities reduce contributor engagement and threaten project sustainability. Preventing such toxicity before it emerges requires…
How Safe Are AI-Generated Patches? A Large-scale Study on Security Risks in LLM and Agentic Automated Program Repair on SWE-bench
Amirali Sajadi, Kostadin Damevski, Preetha Chatterjee
Large language models (LLMs) and their agentic frameworks are increasingly adopted to perform development tasks such as automated program repair (APR). While prior work has identif…
Psycholinguistic Analyses in Software Engineering Text: A Systematic Literature Review
Amirali Sajadi, Kostadin Damevski, Preetha Chatterjee
Context: A deeper understanding of human factors in software engineering (SE) is essential for improving team collaboration, decision-making, and productivity. Communication channe…
Do LLMs Consider Security? An Empirical Study on Responses to Programming Questions
Amirali Sajadi, Binh Le, Anh Nguyen +2
The widespread adoption of conversational LLMs for software development has raised new security concerns regarding the safety of LLM-generated content. Our motivational study outli…
Understanding and Predicting Derailment in Toxic Conversations on GitHub
Mia Mohammad Imran, Robert Zita, Rebekah Copeland +3
Software projects thrive on the involvement and contributions of individuals from different backgrounds. However, toxic language and negative interactions can hinder the participat…
Improving Data Curation of Software Vulnerability Patches through Uncertainty Quantification
Hui Chen, Yunhua Zhao, Kostadin Damevski
The changesets (or patches) that fix open source software vulnerabilities form critical datasets for various machine learning security-enhancing applications, such as automated vul…
Incivility in Open Source Projects: A Comprehensive Annotated Dataset of Locked GitHub Issue Threads
Ramtin Ehsani, Mia Mohammad Imran, Robert Zita +2
In the dynamic landscape of open source software (OSS) development, understanding and addressing incivility within issue discussions is crucial for fostering healthy and productive…
Shedding Light on Software Engineering-specific Metaphors and Idioms
Mia Mohammad Imran, Preetha Chatterjee, Kostadin Damevski
Use of figurative language, such as metaphors and idioms, is common in our daily-life communications, and it can also be found in Software Engineering (SE) channels, such as commen…
Uncovering the Causes of Emotions in Software Developer Communication Using Zero-shot LLMs
Mia Mohammad Imran, Preetha Chatterjee, Kostadin Damevski
Understanding and identifying the causes behind developers' emotions (e.g., Frustration caused by `delays in merging pull requests') can be crucial towards finding solutions to pro…
Interpersonal Trust in OSS: Exploring Dimensions of Trust in GitHub Pull Requests
Amirali Sajadi, Kostadin Damevski, Preetha Chatterjee
Interpersonal trust plays a crucial role in facilitating collaborative tasks, such as software development. While previous research recognizes the significance of trust in an organ…
Towards Understanding Emotions in Informal Developer Interactions: A Gitter Chat Study
Amirali Sajadi, Kostadin Damevski, Preetha Chatterjee
Emotions play a significant role in teamwork and collaborative activities like software development. While researchers have analyzed developer emotions in various software artifact…
Too Few Bug Reports? Exploring Data Augmentation for Improved Changeset-based Bug Localization
Agnieszka Ciborowska, Kostadin Damevski
Modern Deep Learning (DL) architectures based on transformers (e.g., BERT, RoBERTa) are exhibiting performance improvements across a number of natural language tasks. While such DL…
Data Augmentation for Improving Emotion Recognition in Software Engineering Communication
Mia Mohammad Imran, Yashasvi Jain, Preetha Chatterjee +1
Emotions (e.g., Joy, Anger) are prevalent in daily software engineering (SE) activities, and are known to be significant indicators of work productivity (e.g., bug fixing efficienc…
Using clarification questions to improve software developers' Web search
Mia Mohammad Imran, Kostadin Damevski
Context: Recent research indicates that Web queries written by software developers are not very successful in retrieving relevant results, performing measurably worse compared to g…
Online Adaptable Bug Localization for Rapidly Evolving Software
Agnieszka Ciborowska, Michael J. Decker, Kostadin Damevski
Bug localization aims to reduce debugging time by recommending program elements that are relevant for a specific bug report. To date, researchers have primarily addressed this prob…
Fast Changeset-based Bug Localization with BERT
Agnieszka Ciborowska, Kostadin Damevski
Automatically localizing software bugs to the changesets that induced them has the potential to improve software developer efficiency and to positively affect software quality. To…
Modeling Hierarchical Usage Context for Software Exceptions based on Interaction Data
Hui Chen, Kostadin Damevski, David Shepherd +1
Traces of user interactions with a software system, captured in production, are commonly used as an input source for user experience testing. In this paper, we present an alternati…