arXiv subjects

Julien Albert

Publications and source records attributed to Julien Albert.

3 recordsLinked to original sources

arXiv2026

How Developers Experience Debugging Unfamiliar Codebases with Code Tours Generated and Evaluated by Local LLMs

Code tours are interactive, onboarding documentation to guide developers through a codebase. Large Language Models (LLMs) can automatically synthesize code tours. Prior work on code tour generation has not studied developer experience or trust calibration when debugging unfamiliar codebases with code tours generated and evaluated by open-weight LLMs. This study surveys how the properties of components in open-weight LLM-authored code tours influence developers' experiences when debugging unfamiliar codebases. We built a pipeline that generated and evaluated code tours from real reproducible bugs. 26 developers with varying backgrounds participated in a user study. In total, 26 code tours were authored from real Java bugs mined from 2025 GitHub commits, with each tour independently judged by two different LLMs, resulting in 52 evaluated configurations. Participants thought aloud as they explored each tour. Three authors qualitatively coded the interviews to identify recurring themes. Developers generally preferred tours that scaled detail with the code length, avoided merely restating code, were easily scannable, and adopted a guiding tone. However, some preferences were mutually exclusive, such as the use of imperative mood. Stack traces were often insufficient to identify all steps developers found relevant. Developers also trusted descriptions they perceived as human-written more than those they believed were AI-generated. Finally, LLM-generated annotations of tour quality were unreliable: sycophancy, confabulation, and incoherence were pervasive. This work lays a basis for future research on fine-tuning open-weight models for code tour generation, personalizing generation to accommodate diverging preferences, selecting relevant steps beyond stack traces, calibrating users' trust to avoid both disuse and misuse, and improving open-weight LLMs' ability to be more trustworthy evaluators

cs.SE↗

arXiv2024

User Preferences for Large Language Model versus Template-Based Explanations of Movie Recommendations: A Pilot Study

Recommender systems have become integral to our digital experiences, from online shopping to streaming platforms. Still, the rationale behind their suggestions often remains opaque to users. While some systems employ a graph-based approach, offering inherent explainability through paths associating recommended items and seed items, non-experts could not easily understand these explanations. A popular alternative is to convert graph-based explanations into textual ones using a template and an algorithm, which we denote here as ''template-based'' explanations. Yet, these can sometimes come across as impersonal or uninspiring. A novel method would be to employ large language models (LLMs) for this purpose, which we denote as ''LLM-based''. To assess the effectiveness of LLMs in generating more resonant explanations, we conducted a pilot study with 25 participants. They were presented with three explanations: (1) traditional template-based, (2) LLM-based rephrasing of the template output, and (3) purely LLM-based explanations derived from the graph-based explanations. Although subject to high variance, preliminary findings suggest that LLM-based explanations may provide a richer and more engaging user experience, further aligning with user expectations. This study sheds light on the potential limitations of current explanation methods and offers promising directions for leveraging large language models to improve user satisfaction and trust in recommender systems.

cs.IR↗

arXiv2023

An Experimental Investigation into the Evaluation of Explainability Methods

EXplainable Artificial Intelligence (XAI) aims to help users to grasp the reasoning behind the predictions of an Artificial Intelligence (AI) system. Many XAI approaches have emerged in recent years. Consequently, a subfield related to the evaluation of XAI methods has gained considerable attention, with the aim to determine which methods provide the best explanation using various approaches and criteria. However, the literature lacks a comparison of the evaluation metrics themselves, that one can use to evaluate XAI methods. This work aims to fill this gap by comparing 14 different metrics when applied to nine state-of-the-art XAI methods and three dummy methods (e.g., random saliency maps) used as references. Experimental results show which of these metrics produces highly correlated results, indicating potential redundancy. We also demonstrate the significant impact of varying the baseline hyperparameter on the evaluation metric values. Finally, we use dummy methods to assess the reliability of metrics in terms of ranking, pointing out their limitations.

cs.LG↗