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AI in Software Engineering: Ensuring Quality, Reliability, and Security

From Requirements to Maintenance — Challenges and Solutions

An edited volume offering a comprehensive, lifecycle-wide examination of how generative AI and large language models are transforming software engineering — pairing every risk with concrete, evaluated solutions.

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About the Volume

A lifecycle-wide view of trustworthy AI in software engineering

Artificial intelligence — particularly generative AI and large language models — is no longer a peripheral tool in software engineering. From automated requirements analysis and AI-driven design to code generation, test creation, bug repair, and runtime adaptation, AI is now integral to the development process.

Yet the quality, reliability, and security of AI-assisted software remain open challenges. Unlike publications that focus narrowly on AI-generated code, this volume spans every phase — from requirements to maintenance — and pairs each identified risk with concrete, evaluated solutions, industrial case studies, and comparative benchmarks of major AI coding models.

It serves as an essential interdisciplinary reference for researchers, software engineers, quality assurance professionals, and policymakers navigating AI-assisted and AI-driven software engineering.

Key challenges addressed

Functional incorrectness & specification non-conformance
Security vulnerabilities & adversarial attacks
Performance and efficiency pitfalls
Maintainability and technical debt
Testing and validation gaps
Legal, ethical, and licensing risks
Operational and deployment risks

Scope

Themes & Topics of Interest

We welcome original contributions across the following themes. Topics are indicative, not exhaustive.

AI in Requirements & Design

Automated requirements extraction, ambiguity detection, specification mining, and AI-assisted architectural design risks.

AI-Generated Code: Quality & Security

Functional correctness, logic errors, security vulnerabilities, adversarial attacks, performance pitfalls, and automatic repair.

Testing, Verification & Validation

Automated test generation with LLMs, metamorphic and property-based testing, formal verification, and the oracle problem.

Deployment, Maintenance & Operations

Auto-repair and CI/CD risks, runtime monitoring, maintainability, technical debt, and runtime security.

Ethical, Legal & Governance

Industrial Experiences & Case Studies

Real-world deployments and benchmarking of AI coding models (Copilot, ChatGPT, StarCoder, CodeLlama) with cost-benefit analyses.

Timeline

Important Dates

Aug 15, 2026

Abstract / chapter proposal (300 words) — optional

Sep 30, 2026

Full chapter submission deadline

Oct 31, 2026

Notification of acceptance / review feedback

Nov 30, 2026

Camera-ready (final) chapter

Q1 2027

Expected publication by Springer

Dates may be adjusted to ensure quality assurance; please check this page for updates.

Editorial Team

Edited by

Prof. Moez Krichen

Lead Editor

Albaha University, KSA · ReDCAD Lab, Sfax

Prof. Anis Koubaa

Co-Editor

Alfaisal University, Riyadh, KSA

Dr. Mariam Lahami

Co-Editor

ReDCAD Lab, Sfax University, Tunisia

Dr. Mohamed Bahloul

Co-Editor

Alfaisal University, Riyadh, KSA

Prof. Driss Benhaddou

Co-Editor

Alfaisal University, Riyadh, KSA

How to Contribute

Submission guidelines

Prepare your manuscript

Original, unpublished chapters in English, recommended maximum 30 pages in Springer format. Not under consideration elsewhere.

Use the Springer template

Format with the official Springer book chapter template — LaTeX or Word.

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All submissions are managed through the ReviewPilot system. Create an account and submit your abstract and full chapter to the event.

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