AISEL explores the convergence of Artificial Intelligence and Software Engineering to transform how software is understood, developed, and evolved. Through Automated Software Engineering (ASE), data-driven software repository intelligence, and Natural Language-based Software Engineering (NLbSE), we develop AI systems that learn from software artifacts, understand engineering context, and turn software data into actionable knowledge. Our research aims to advance trustworthy and human-centered AI toward intelligent systems capable of assisting, reasoning, and autonomously performing complex software engineering tasks.
Discover our research directions at the intersection of Artificial Intelligence, Software Engineering, and Data-Driven Analytics. AISEL develops intelligent methods and practical tools that automate software engineering, understand user and developer feedback, and transform complex data into actionable knowledge.
Software Engineering for AI (SE4AI) focuses on engineering reliable, maintainable, and trustworthy AI-enabled software systems. Our research investigates software development, quality assurance, testing, monitoring, and lifecycle challenges that emerge when AI becomes an integral part of modern software applications.
AI for Software Engineering (AI4SE) applies artificial intelligence, machine learning, natural language processing, and large language models to support and automate software engineering activities. Our research explores intelligent assistance for requirements, coding, testing, maintenance, software analytics, and developer decision-making.
Automated Software Engineering develops intelligent techniques for automating repetitive and complex software engineering tasks. Our work includes automated program analysis, bug and issue classification, program repair, recommendation systems, repository mining, and AI-driven software development workflows.
App Reviews Analytics uses natural language processing and machine learning to transform large collections of mobile application reviews into actionable software engineering knowledge. Our research analyzes user feedback to identify feature requests, quality concerns, problems, sentiment, sarcasm, and other signals supporting software evolution.
EHealth Analytics explores AI and data-driven approaches for extracting meaningful insights from healthcare data. Our research investigates intelligent analytics, predictive modeling, natural language processing, and decision-support techniques that can improve healthcare processes, information management, and digital health services.
Electronic Health Records (EHRs) focuses on intelligent analysis and management of digitally stored clinical information. Our research explores AI-based methods for extracting knowledge from clinical records, supporting healthcare decision-making, improving information accessibility, and enabling effective use of structured and unstructured health data.
Our research is grounded in the intersection of Artificial Intelligence and Software Engineering. We investigate how software artifacts—including requirements, source code, repositories, developer interactions, architectural decisions, and user feedback—can be transformed into actionable engineering intelligence. By combining natural language processing, machine learning, deep learning, foundation models, and agentic AI, we seek to move software engineering from conventional automation toward context-aware, evidence-driven, and increasingly autonomous decision-making.
Software systems are not merely collections of source code. Requirements, issues, commits, pull requests, documentation, architectural decisions, developer discussions, and user feedback collectively represent the evolving knowledge of a software project. Our research explores how to mine, integrate, and reason over this distributed software knowledge to uncover patterns, relationships, and engineering insights that are difficult to identify through conventional analysis.
A substantial portion of software knowledge is expressed through natural language. Requirements, issue descriptions, documentation, developer discussions, and user reviews encode information about functionality, quality, risks, expectations, and engineering decisions. Our research investigates NLP and language-model based approaches for transforming unstructured human language into structured and actionable software engineering knowledge.
AI-based software engineering should not rely solely on model predictions. Our research emphasizes combining empirical evidence from software repositories with machine learning, deep learning, and language models to produce more informed recommendations, predictions, and engineering decisions. The objective is to transform historical and continuously generated software data into reliable, evidence-based engineering intelligence.
Traditional software automation primarily executes predefined rules and workflows. Our research investigates the next step: systems capable of understanding context, reasoning about engineering problems, generating candidate solutions, evaluating alternatives, and adapting their actions. This creates a pathway from conventional automated tools toward intelligent, reasoning-driven, and agentic software engineering systems.
AI should augment software engineers rather than simply replace human decision-making. Our research therefore considers explainability, reliability, trustworthiness, human oversight, and practical usefulness as important dimensions of AI-driven software engineering. The goal is to create intelligent systems whose recommendations and actions can be understood, evaluated, and meaningfully integrated into real software development practices.
Our long-term research vision is to develop an intelligent software engineering ecosystem in which AI can continuously learn from software artifacts, understand human and technical context, reason over engineering evidence, and autonomously assist in software development while preserving human control and trust. In this vision, the software repository becomes a living source of engineering knowledge, while NLP, foundation models, analytics, and agentic AI provide mechanisms for understanding, reasoning, and action.
From artifacts to knowledge, from knowledge to intelligence, and from intelligence to autonomous software engineering.
Selected research projects demonstrating the application of our methods across software engineering, intelligent systems, application analytics, and related domains.
AISEL brings together researchers, research assistants, academic collaborators, and alumni working across AI-driven Software Engineering, natural language-based software engineering, repository analytics, requirements engineering, software architecture, cybersecurity, and intelligent systems.
AISEL develops data-driven and AI-enabled approaches for understanding, automating, and improving software engineering activities. The lab brings together machine learning, NLP, transformer-based language models, LLMs, repository mining, agentic AI, requirements intelligence, software quality, and empirical software engineering.
Our people contribute from different perspectives—from core lab research to international collaboration—creating a network that connects ideas, methods, datasets, tools, and real-world software engineering problems.
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Academic leadership, research direction, and the long-term vision of AISEL.
ASSOCIATE PROFESSOR · AL AIN UNIVERSITY
Principal Investigator, AISEL Lab
Abu Dhabi, United Arab Emirates
Dr. Khubaib Amjad Alam is an Associate Professor at Al Ain University and Principal Investigator of AISEL. His research interests include Automated Software Engineering, AI methodologies for Software Engineering, natural language-based Software Engineering, requirements engineering, software architecture, software maintenance and evolution, software quality assurance, transformer-based language models, data-driven analytics, and decision support systems.
Researchers contributing to AI-driven Software Engineering, NLP, software analytics, and intelligent systems.
Senior Researcher
AISEL - Lab
Muhammad Haroon is a Senior Researcher at AISEL working on AI-driven Software Engineering and natural language-based software engineering. His work explores language models, agentic AI, repository mining, app review analytics, software quality analysis, requirements intelligence, and automated software engineering tasks.
Research Associate
AISEL - Lab
Momina Kamal Cheema is a Research Associate at AISEL. Her research contributions include natural language-based Software Engineering and the analysis of mobile app reviews using transformer-based language models, with a focus on turning complex user feedback into useful software engineering insights.
Research Associate
AISEL - Lab
Maryam Hussain is a Software Engineering researcher and instructor whose work includes data-driven software feedback analysis, app review analytics, software quality concerns, and human-centered AI for Requirements Engineering. She also contributes to collaborative research communication and AI-enabled Software Engineering initiatives.
Research Assistant
AISEL - Lab
Katrina Bodani is a Research Assistant at AISEL. Her public project work includes multimodal AI, retrieval-augmented generation, joint text-image embeddings, and LLM-based applications. She contributes to applied AI research and intelligent systems that combine language, retrieval, and multimodal information.
Academic partners extending AISEL across security, requirements, architecture, intelligent systems, and data-driven software engineering.
Associate Professor
UNIVERSITY COLLEGE DUBLIN · IRELAND
Dr. Liliana Pasquale is an Associate Professor at University College Dublin and a funded investigator at Lero. Her research focuses on adaptive, human-centred security systems that detect, diagnose, and respond to evolving cyber threats while preserving human agency and ethical oversight. She also works on ransomware defence and regulatory-compliant software engineering.
Professor · Software & IT Engineering
ÉTS MONTRÉAL · CANADA
Dr. Imen Benzarti is a professor in the Software and Information Technology Engineering Department at ÉTS Montréal. Her research areas include intelligent and autonomous systems, software systems, multimedia and cybersecurity, requirements engineering, customer and user experience, model-driven software engineering, and Internet of Things.
Associate Professor / Senior Lecturer
LINNAEUS UNIVERSITY · SWEDEN
Dr. Nadeem Abbas is an Associate Professor in Software Engineering at Linnaeus University, Sweden. His research focuses on self-adaptive software systems, dynamic software product lines, requirements engineering, software reuse, software architecture and design, and architectural analysis and reasoning, with growing emphasis on AI-supported decision-making and responsible system design.
Associate Professor
Al Ain University · Abu Dhabi
Dr. Haroon Mahmood is an Associate Professor at Al Ain University. His research interests include information security, Internet of Things security and reliability, security auditing and vulnerability analysis, digital forensics, differential privacy, artificial intelligence, UAV mobility modelling, software-defined networking, and large language models.
Former members who remain part of the wider research community and continue their academic or professional journeys.
MS Student · AISEL Alumni
Former AISEL Research Member
Nadia Bashir is listed in the supplied AISEL alumni record as an MS student and former member of the research community. The available profile information does not specify a current affiliation or additional research interests, so this profile intentionally avoids adding unverified details.
Explore AISEL's research contributions across Artificial Intelligence, Automated Software Engineering, Natural Language-Based Software Engineering, Repository Analytics, Requirements Engineering, Software Quality, and Intelligent Systems.
Explore PublicationsBrowse the latest AISEL publications by type, year, author, venue, or keyword. Each entry opens a concise research record with a direct link to the publication.
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Explore our research projects that combine Transformers, Large Language Models, Agentic AI, and Inference Optimization to solve challenging software-engineering problems.
Explore Research ProjectsOur projects address practical software-engineering challenges through intelligent language models, deep learning, agentic reasoning, and efficient AI inference.
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Follow AISEL's latest research milestones, workshop acceptances, collaborations, and initiatives advancing AI-driven Software Engineering.
Explore Latest UpdatesAISEL contributes to the international Software Engineering research community through workshop organization, conference collaboration, research initiatives, and scientific activities that advance Artificial Intelligence for Software Engineering.
Research. Collaboration. Impact. AISEL continues moving intelligent Software Engineering forward.
AISEL brings together researchers, academic collaborators, and Software Engineering experts working across Artificial Intelligence, adaptive systems, requirements engineering, security, and intelligent systems.
Explore Academic VoicesAISEL's wider research network connects academics working across complementary areas of Artificial Intelligence and Software Engineering. The profiles below highlight researchers whose expertise contributes to the broader academic ecosystem.
Research · Collaboration · Impact
Connecting ideas, expertise, and people across
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Interested in research collaboration, graduate opportunities, intelligent software engineering, AI-driven software engineering, or industry partnerships? Connect with AISEL and explore opportunities to work together on meaningful research, intelligent systems, and software engineering innovation.
Whether you are interested in research collaboration, graduate opportunities, software engineering research, workshop proposals, or industry partnerships, AISEL welcomes meaningful conversations that can lead to new research ideas, collaborations, and intelligent software engineering solutions.
General questions, research collaborations, media inquiries, workshop proposals, or industry partnerships are welcome. Your message can be directed to the AISEL research team so that we can better understand your interests and identify appropriate opportunities for collaboration.
Have an idea, research problem, dataset, collaboration proposal, or potential project? We would be glad to hear from you.
AISEL welcomes applications from motivated PhD, MS, undergraduate researchers, and potential industry collaborators interested in AI-driven Software Engineering. Tell us about your background, interests, and the research direction you would like to explore.
Explore research problems in AI-driven Software Engineering and related areas.
Work with researchers on interdisciplinary software engineering problems.
Turn research ideas into publications, prototypes, and practical systems.
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