The 7th ACM/IEEE International Conference on Automation of Software Test (AST 2026)

Software pervasiveness in both industry and digital society, as well as the proliferation of Artificial Intelligence (AI) technologies are continuously leading to emerging needs from both software producers and consumers. Infrastructures, software components, and applications aim to hide their increasing complexity in order to appear more human-centric. However, the potential risk from design errors, poor integrations, and time-consuming engineering phases can result in unreliable solutions that can barely meet their intended objectives. In this context, Software Engineering processes keep demanding for the investigation of novel and further refined approaches to Software Quality Assurance (SQA). Software testing automation is a discipline that has produced noteworthy research in the last decade. The search for solutions to automatically test any concept of software is critical, and it encompasses several areas: from the generation of the test cases, test oracles, test stubs/mocks; through the definition of selection and prioritization criteria; up to the engineering of infrastructures governing the execution of testing sessions locally or remotely in the cloud.

AST continues with a long record of international scientific forums on methods and solutions to automate software testing. This year AST is the 7th edition of a conference that was formerly organized as workshops since 2006. The conference promotes high quality research contributions on methods for software test automation, and original case studies reporting practices in this field. We invite contributions that focus on: i) lessons learned about experiments of automatic testing in practice; ii) experiences of the adoption of testing tools, methods and techniques; iii) best practices to follow in testing and their measurable consequences; and iv) theoretical approaches that are applicable to the industry in the context of AST.

Authors of the best papers presented at AST 2026 will be invited to submit an extension of their work for possible inclusion in a special issue of the Journal of Systems and Software (JSS).

Call for papers.

Accepted Papers

	Title
	ACT: Automated CPS Testing for Open-Source Robotic Platforms AST 2026 Aditya A. Krishnan, Donghoon Kim, Hokeun Kim DOI Pre-print
	A Framework for Similarity-based and Resource-aware Orchestration of End-to-End Test Cases AST 2026 Cristian Augusto, Antonia Bertolino, Guglielmo De Angelis, Claudio de la Riva, Francesca Lonetti, Jesús Morán
	APITestGenie: Generating Web API Tests from Requirements and API Specifications with LLMs AST 2026 André Pereira, Bruno Lima, João Pascoal Faria Pre-print
	A Unified Benchmark for Out-of-Distribution Detection for Autonomous Driving Systems AST 2026 Xiangyu Li, Jingyu ZHANG, Jacky Keung, Xiaoxue Ma, Yihan Liao Pre-print Media Attached
	Exploring Mocking Techniques for Managing External Dependencies in Service-Based Systems: A Mapping Study AST 2026 Benedito Fernando Albuquerque de Oliveira, Fernando Castor, Leo Fernandes, Samuel Amorim
	From Logs to Lessons: An Exploration of LLM-based Log Summarization for Debugging Automotive Software AST 2026 Anton Ekström, Hampus Rhedin Stam, Francisco Gomes de Oliveira Neto, Gregory Gay, Sabina Edenlund
	HYDRA: A Hybrid Heuristic-Guided Deep Representation Architecture for Predicting Latent Zero-Day Vulnerabilities in Patched Functions AST 2026 Mohammad Farhad, Sabbir Rahman, Shuvalaxmi Dass Pre-print Media Attached
	Improving Deep Learning Library Testing with Machine Learning AST 2026 Facundo Molina, M M Abid Naziri, Feiran Qin, Alessandra Gorla, Marcelo d'Amorim
	LLAMAFUZZ: Large Language Model Enhanced Greybox Fuzzing AST 2026 Hongxiang Zhang, Yuyang Rong, Yifeng He, Hao Chen Pre-print Media Attached
	L-SCALE: Locality-Sensitive Coverage for Automata LEarning AST 2026 Mark Giraud, Bastian Engel, Lea Nasarek, Yannis Storrer, Philipp Takacs, Leon Philipp Wittemund
	REST-at: An LLM-Based Tool for Automating Traceability between Requirements and Test Cases AST 2026 Nicole Leon-Quinstedt, Bao Lindgren, Mert Yurdakul, Francisco Gomes de Oliveira Neto
	Search-Based Fuzzing For RESTful APIs That Use MongoDB AST 2026 Hernan Ghianni, Man Zhang, Juan Pablo Galeotti, Andrea Arcuri
	Separating Valid from Invalid Inputs for a Digital Aircraft Design Tool AST 2026 Malte Christian Struck, Andreas Schuster, Alexander Weinert, Michael Felderer
	Software Testing Education in the LLM Era: Insights and Emerging Theory AST 2026 Samhitha Dwarakanath, Nathalia Nascimento, Everton Guimaraes
	Testing Framework Migration with Large Language Models AST 2026 Altino Alves Júnior, João Eduardo Montandon, Andre Hora Pre-print Media Attached
	Understanding and Detecting Platform-Specific Violations in Android Auto Apps AST 2026 Moshood Fakorede, Umar Farooq Pre-print Media Attached
	Understanding Bug-Reproducing Tests: A First Empirical Study AST 2026 Andre Hora, Gordon Fraser Pre-print Media Attached
	Understanding on the Edge: LLM-generated Boundary Test Explanations AST 2026 Sabina Akbarova, Felix Dobslaw, Robert Feldt Pre-print

Call for Papers

Important Information

Starting 2026, all articles published by ACM will be made Open Access. This is greatly beneficial to the advancement of computer science and leads to increased usage and citation of research. Most authors will be covered by ACM OPEN agreements by that point and will not have to pay Article Processing Charges (APC). Check if your institution participates in ACM OPEN. Authors not covered by ACM OPEN agreements may have to pay APC; however, ACM is offering several automated and discretionary APC Waivers and Discounts.

Submissions must follow the latest policies from IEEE and ACM (IEEE Submission and Peer Review Policy and ACM Policy on Authorship, with associated FAQ), which includes a policy specific to the use of generative AI tools and technologies, such as ChatGPT.

The official publication date is the date the proceedings are made available in the ACM Digital Library. This date may be up to two weeks prior to the first day of ICSE 2026. The official publication date affects the deadline for any patent filings related to published work.

Purchases of additional pages in the proceedings are not allowed.

Scope

Software pervasiveness in both industry and digital society, as well as the proliferation of Artificial Intelligence (AI) technologies are continuously leading to emerging needs from both software producers and consumers. Infrastructures, software components, and applications aim to hide their increasing complexity in order to appear more human-centric. However, the potential risk from design errors, poor integrations, and time-consuming engineering phases can result in unreliable solutions that can barely meet their intended objectives. In this context, Software Engineering processes and methods keep demanding for the investigation of novel and further refined approaches to Software Quality Assurance (SQA).

Software testing automation is a discipline that has produced noteworthy research in the last decades. The search for solutions to automatically test any concept of software is critical, and it encompasses several areas: from the generation of the test cases, test oracles, test stubs/mocks; through the definition of selection and prioritization criteria; up to the engineering of infrastructures governing the execution of testing sessions locally or remotely in the cloud.

The Automation of Software Test (AST) conference continues with a long record of international scientific forums on methods and solutions to automate software testing. In 2026, AST is in its 7th edition of a conference that was formerly organized as workshops since 2006. The conference promotes high-quality research contributions on methods for software test automation, and original case studies reporting practices in this field. We invite contributions that focus on: i) lessons learned about experiments of automatic testing in practice; ii) experiences of the adoption of testing tools, methods and techniques; iii) best practices to follow in testing and their measurable consequences; and iv) theoretical approaches that are applicable to the industry in the context of AST.

Our theme for this year is “Adapting test automation to the AI era including sustainability and emerging challenges” with special sessions on test automation for AI, sustainability, and AI testing education and training.

Topics of Interest

Submissions on the AST 2026 theme are especially encouraged, but papers on other topics relevant to the automation of software tests are also welcome. Topics of interest include, but are not limited to the following:

• AI for Automated Software Testing
• Testing of AI-based systems
• Effective testing through explainable AI
• Education to software testing with the advent of AI-based technology
• Automated software testing for sustainability, resource efficiency, energy consumption
• Codeless and low code test automation
• Test Automation in Software Process and Evolution, DevOps, Agile, CI/CD flows
• Test-driven development
• Model-based testing
• Formal methods, model checking and theories for testing and test automation
• Test case generation based on formal, semi-formal and AI models
• Theoretical foundations and methods for test automation
• Standardization of test languages, processes and tools
• Security, performance, and robustness testing
• Functional, interoperability, and conformance testing
• Usability and user experience testing
• Software simulation and automated software testing
• Testing of embedded, reactive and object-oriented systems
• Test automation of large complex systems
• Testing cyber physical systems
• Testing cloud and web systems and services
• Product line testing
• Testing anomaly detectors
• Metrics for testing - test effectiveness, efficiency, test coverage
• Testing tools
• Testing in different industry domains including healthcare, energy, finance, etc.

Important Dates

Submission: 26 October 2025

Notification: 05 January 2026

Final version: 26 January 2026

Conference: 13-14 April 2026

Organizers

Breno Miranda, Federal University of Pernambuco, Brazil (General Chair)

Markus Borg, CodeScene & Lund University, Sweden (Program Co-Chair)

Ana Paiva, University of Porto, Portugal (Program Co-Chair)

Andy Zaidman, Delft University of Technology, The Netherlands (Program Co-Chair)

Submission Details

Three types of submissions are invited for both research and industry:

1. Regular Papers (up to 10 pages plus 2 additional pages of references)
2. Short papers in the format of 5-page extended abstracts.
3. Industrial extended abstracts (up to 2 pages for all materials)

The submission website is: https://ast2026.hotcrp.com

All submissions must adhere to the following requirements: The page limit is strict (10 pages plus 2 additional pages of references for full papers; 4 pages plus 1 additional page of references for short papers; 2 pages for all materials in case of industrial abstracts). It will not be possible to purchase additional pages at any point in the process (including after acceptance).

Submissions must strictly conform to the [ACM formatting guidelines[(http://www.acm.org/publications/proceedings-template). All submissions must be in PDF.

LaTEX users must use the provided acmart.cls and ACM-Reference-Format.bst without modification, enable the conference format in the preamble of the document (i.e. \documentclass[sigconf,review]{acmart}), and use the ACM reference format for the bibliography (i.e., \bibliographystyle{ACM-Reference-Format}).

Submissions must be unpublished original work and should not be under review or submitted elsewhere while being under consideration. AST 2026 will follow the double-blind review process. The accepted regular papers, short papers, and industrial abstracts will be published in the ICSE 2026 Co-located Event Proceedings and included in the IEEE and ACM Digital Libraries. The official publication date is the date the proceedings are made available in the ACM Digital Library. This date may be up to two weeks prior to the first day of ICSE 2026. The official publication date affects the deadline for any patent filings related to published work.

Special Issue

Authors of the best papers presented at AST 2026 will be invited to submit an extension of their work for possible inclusion in a special issue.

https://conf.researchr.org/attending/icse-2026/registration

• 2025 - Ottawa, Canada
• 2024 - Lisbon, Portugal
• 2023 - Melbourne, Australia
• 2022 - Pittsburgh, USA
• 2021 - Virtual (originally in Madrid, Spain)
• 2020 - Seoul - South Korea
• 2019 - Montreal - Canada
• 2018 - Gothenburg - Sweden
• 2017 - Buenos Aires - Argentina
• 2016 - Austin - Texas
• 2015 - Firenze - Italy
• 2014 - Hyderabad - India
• 2013 - San Jose California - USA
• 2012 - Zurich - Switzerland
• 2011 - Waikiki - Hawaii
• 2010 - Cape Town - South Africa
• 2009 - Vancouver - Canada
• 2008 - Leipzig - Germany
• 2007 - Minneapolis - USA
• 2006 - Shanghai - China

Gunel Jahangirova, King’s College London

Title: Deep Learning Fault Localisation and Repair: Benchmarks, Limitations, and the Role of LLMs

When: Mon 13 Apr

Abstract: As Deep Learning (DL) systems become increasingly pervasive in safety-critical and high-impact domains, the need for effective techniques to test, localise, and repair faults in Deep Neural Networks (DNNs) has never been greater. Over the past few years, numerous fault localisation (FL) and repair approaches have been proposed, leveraging both static and dynamic analyses, as well as rule-based heuristics. However, a fundamental question remains: how effective and reliable are these techniques in practice?

In this talk, I will present a comprehensive empirical investigation into the current state of fault localisation and repair for DL systems. First, I will discuss a large-scale comparative evaluation of state-of-the-art FL techniques, conducted on a benchmark comprising both real-world faults collected from bug reporting platforms and faults generated via mutation testing. Our findings reveal that current techniques struggle to achieve strong and consistent performance when evaluated against a single human-defined ground truth, raising concerns about how effectiveness is currently assessed. Next, I will examine the broader ecosystem of DL fault localisation and repair techniques, highlighting their strengths and limitations. I will then present an empirical study investigating whether Large Language Models (LLMs) can effectively localise and repair faults in DL systems. Our evaluation shows that LLMs demonstrate strong performance compared to existing approaches, suggesting that they may offer a promising direction for advancing automated DL debugging. Finally, I will address a critical but often overlooked issue: the realism and reproducibility of existing DL fault benchmarks that are used to evaluate DL faults localisation and repair approaches. Through a manual analysis of hundreds of reported faults across widely used benchmarks, we find that only a limited subset satisfies strong realism criteria, and reproducibility remains a significant challenge. These findings raise important concerns about current evaluation practices and underscore the need for more rigorous assessment methodologies.

Bio: Gunel Jahangirova is a Lecturer (Assistant Professor) at King’s College London (KCL), United Kingdom. Prior to joining KCL, she was a Postdoctoral Researcher at Università della Svizzera Italiana (USI) in Lugano, Switzerland. She obtained her PhD through a joint programme between Fondazione Bruno Kessler (FBK) in Trento, Italy, and University College London (UCL), UK. Her research focuses on the automatic generation and evaluation of test oracles, error propagation in software systems, testing of deep learning systems, oracle design and quality metrics for autonomous vehicles, and the application of artificial intelligence to software engineering tasks.

Keynote by Marcelo José Ruv Lemes, EMBRAER

Title: Verification of Embedded Software for Aeronautical Applications

When: Tue 14 Apr

Abstract: The development of embedded software for aeronautical applications is carried out in a highly regulated environment, with one of the main activities being the verification. Software verification in this context encompasses reviews, analyses, and testing. This presentation aims to provide an overview of verification activities for embedded software for aeronautical applications, placing them within the broader context of aircraft verification.

Bio: Marcelo Lemes holds a degree in Data Processing Technology and Mathematics from the University of Taubaté. He also holds a Master degree in Software Engineering from the Aeronautics Institute of Technology and a Doctorate in Digital Systems from the Polytechnic School of the University of São Paulo. He worked for 12 years at the Institute of Aeronautics and Space (IAE) of the Aerospace Technical Center (CTA), spending most of the time involved in the development of embedded software for the Brazilian Satellite Launch Vehicle (VLS). Since 1997, he has worked at EMBRAER involved with development and certification of embedded software applications. He currently works alongside the company’s Chief Engineer, coordinating embedded software activities for the company.