1st Workshop on Evaluation of Qualitative Aspects of Intelligent Software Assistants (EQUISA) - EASE 2025

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Workshops Details
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Intelligent software assistants have been defined as automated approaches based on advanced artificial intelligence (AI) models that aim to support end users in several aspects of software development lifecycles. While traditional systems are based on a curated knowledge base that represents the main source of the recommendation process, the advent of cutting-edge AI models, e.g., foundation and pre-trained models, is dramatically changing how those systems are designed, developed, and evaluated.

Motivated by the need to ensure quality in advanced systems, EQUISA is a dedicated forum for discussing the qualitative aspects of intelligent software assistants, from their design to their deployment in real-world applications.

Topics of interest include, but are not limited to, the following:

- Re-usage of AI-based tools, techniques, and methodologies in developing intelligent software assistants.

- Foundational theories for software assistant to understand the underlying principles that can drive the development of more robust and generalizable recommendation systems in software engineering, with a focus on their evaluation.

- Evaluating quality aspects of software assistants, e.g., explainability, transparency, and fairness, ensuring that software assistants produce reliable results.

- New methods, tools, and frameworks to support development tasks, e.g., code-related tasks, automated classification of software artifacts, or code generation leveraging generative AI models.

- Designing specific prompt engineering techniques for intelligent software assistants based on large language models to ensure quality aspects.

- Data-driven approaches for software assistant: Leveraging large-scale data from open-source software (OSS) repositories, Q&A forums, and issue trackers to enhance the effectiveness of software assistants.

- Integration with human-in-the-loop systems: Balancing automated recommendations with human expertise to improve decision-making in complex SE scenarios.

- Adoption of advanced generative AI models, including LLMs, pre-trained models (PTMs) for software assistance, particularly emphasizing the quality effects.

- Empirical studies and controlled experiments to assess qualitative aspects of intelligent systems.

- Evolution of software systems and long-term recommendations, e.g., how software assistants can cope with the evolving nature of software systems and provide recommendations that consider long-term system maintainability and evolution.

- Cross-disciplinary applications of software assistant: Studying how techniques from other domains, e.g., human-computer interaction, natural language processing, and social network analysis, can enhance their effectiveness and usability.

- Surveys and experience reports on software assistants to support software engineering tasks both in academic and industry use cases.

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Important Dates
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- Full paper submission: March 16th, 2025
- Notification: April 13th, 2025
- Camera-ready: April 26th, 2025

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Organizers
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Pablo Gòmez-Abajo (Universidad Autónoma de Madrid, Spain)
Claudio Di Sipio (University of L’Aquila, Italy)
Valeria Pontillo (Vrije Universiteit Brussel, Belgium)
Riccardo Rubei (University of L’Aquila, Italy)