arXiv subjects

Sam Malek

Publications and source records attributed to Sam Malek.

4 recordsLinked to original sources

arXiv2025

Towards Explorative IRBL: Combining Semantic Retrieval with LLM-driven Iterative Code Exploration

Information Retrieval-based Bug Localization (IRBL) aims to identify buggy source files for a given bug report. Traditional and deep learning-based IRBL techniques often suffer from vocabulary mismatch and dependence on project-specific metadata. In contrast, recent Large Language Model (LLM)-based approaches struggle to provide appropriate context to the model: they either restrict analysis to a fixed set of candidate files, overwhelm the model with repository-wide information, or rely on explicit bug report cues to guide context collection. To address these issues, we propose GenLoc, a technique that combines semantic retrieval with LLM-driven code-exploration functions to iteratively analyze the code base and identify buggy files. We evaluate GenLoc on three complementary benchmarks, including large-scale and recent Java datasets as well as the Python based SWE-bench Lite dataset. Results demonstrate that GenLoc substantially outperforms traditional IRBL, deep learning-based approaches and recent LLM-based methods, while also localizing bugs that other techniques fail to detect.

cs.SE↗

arXiv2025

Human or LLM? A Comparative Study on Accessible Code Generation Capability

Web accessibility is essential for inclusive digital experiences, yet the accessibility of LLM-generated code remains underexplored. This paper presents an empirical study comparing the accessibility of web code generated by GPT-4o and Qwen2.5-Coder-32B-Instruct-AWQ against human-written code. Results show that LLMs often produce more accessible code, especially for basic features like color contrast and alternative text, but struggle with complex issues such as ARIA attributes. We also assess advanced prompting strategies (Zero-Shot, Few-Shot, Self-Criticism), finding they offer some gains but are limited. To address these gaps, we introduce FeedA11y, a feedback-driven ReAct-based approach that significantly outperforms other methods in improving accessibility. Our work highlights the promise of LLMs for accessible code generation and emphasizes the need for feedback-based techniques to address persistent challenges.

cs.SE↗

arXiv2024

Automated Test Transfer Across Android Apps Using Large Language Models

The pervasiveness of mobile apps in everyday life necessitates robust testing strategies to ensure quality and efficiency, especially through end-to-end usage-based tests for mobile apps' user interfaces (UIs). However, manually creating and maintaining such tests can be costly for developers. Since many apps share similar functionalities beneath diverse UIs, previous works have shown the possibility of transferring UI tests across different apps within the same domain, thereby eliminating the need for writing the tests manually. However, these methods have struggled to accommodate real-world variations, often facing limitations in scenarios where source and target apps are not very similar or fail to accurately transfer test oracles. This paper introduces an innovative technique, LLMigrate, which leverages Large Language Models (LLMs) to efficiently transfer usage-based UI tests across mobile apps. Our experimental evaluation shows LLMigrate can achieve a 97.5% success rate in automated test transfer, reducing the manual effort required to write tests from scratch by 91.1%. This represents an improvement of 9.1% in success rate and 38.2% in effort reduction compared to the best-performing prior technique, setting a new benchmark for automated test transfer.

cs.SE↗

arXiv2019

Perpetual Assurances for Self-Adaptive Systems

Providing assurances for self-adaptive systems is challenging. A primary underlying problem is uncertainty that may stem from a variety of different sources, ranging from incomplete knowledge to sensor noise and uncertain behavior of humans in the loop. Providing assurances that the self-adaptive system complies with its requirements calls for an enduring process spanning the whole lifetime of the system. In this process, humans and the system jointly derive and integrate new evidence and arguments, which we coined perpetual assurances for self-adaptive systems. In this paper, we provide a background framework and the foundation for perpetual assurances for self-adaptive systems. We elaborate on the concrete challenges of offering perpetual assurances, requirements for solutions, realization techniques and mechanisms to make solutions suitable. We also present benchmark criteria to compare solutions. We then present a concrete exemplar that researchers can use to assess and compare approaches for perpetual assurances for self-adaptation.

cs.SE↗