2nd Workshop on Evaluation of Qualitative Aspects of Intelligent Software Assistants (EQUISA) - EASE 2026

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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:

• Evaluation and assessment of quality aspects of software assistants, e.g., explainability, transparency, and fairness, ensuring that software assistants produce reliable results.

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

• Foundational theories for software assistants 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.

• 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 assistants: 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.
• Low-Code and No-Code approaches to ease the development of intelligent software assistants.

• Adoption of advanced generative AI models, including LLMs, pre-trained models (PTMs) for software assistants, 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.

• Sustainability in design and developing intelligent software assistants.

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

The aim of this workshop is to provide a forum for researchers and practitioners to present and discuss novel methods and techniques for designing, developing, and assessing intelligent software assistants according to notable qualitative aspects. We expect that the workshop will help to:

• Provide researchers with a comprehensive landscape of recent intelligent software assistants.

• Investigate how generative AI models can be used in developing software assistants.

• Reinforce the foundational knowledge around software assistants, with a focus on empirical evaluation, trustworthiness, and ethical considerations.

• Identify new opportunities for applying software assistant research to address the most pressing challenges in modern software engineering.

• Proposing new empirical methodologies, protocols, and metrics to evaluate qualitative aspects.

• Analyze intelligent software assistants in industries and measure their conformity to recent qualitative standards.

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How to Submit
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All papers must be submitted in PDF format through EasyChair. The page limit is set to 10 for full papers, 5 for short papers, and 2 for position papers, including all figures, tables, references, and appendices. All submissions must be submitted in PDF format through EasyChair. All submissions must use the official ACM Primary Article Template.2 Deviating from the ACM formatting instructions may lead to a desk rejection.

Authors must comply with the SIGSOFT Open Science Policy,3 (i.e., to archive data and artifacts in a permanent repository—e.g., Zenodo, not GitHub—and include links in the submission).

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Important Dates
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• Submission deadline: March 2nd, 2026.
• Notification to authors: April 6th, 2026.
• Camera-ready due: April 20th, 2026.


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Organizers
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Claudio Di Sipio (Johanns Kepler University, Austria)
Riccardo Rubei (Mälardalens universitet, Sweden)
Gianmario Voria (Università degli Studi di Salerno, Italy)
Pablo Gómez-Abajo (Universidad Autónoma de Madrid, Spain)
Giordano d’Aloisio (Università degli Studi dell'Aquila, Italy)