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ECOOP 2021
Sun 11 - Sat 17 July 2021 Online
co-located with ECOOP and ISSTA 2021

Artificial Intelligence (AI) has achieved substantial success in enhancing various software testing and program analysis techniques and applications, including but not limited to static analysis, fuzz testing, GUI testing, vulnerability detection, code similarity analysis, software debloating, and patching. We often see a synergistic effect that AI models, by learning from past experience to make decisions, can notably boost conventional program analysis and software testing tasks. Hence, it is a promising direction by applying advanced machine learning techniques into suitable software engineering tasks.

Furthermore, recent years have also witnessed a substantial adoption of AI models in safety- and security-critical applications such as medical image processing, autonomous driving, aircraft control systems, machine translation, and surveillance cameras. Thus, it is also highly crucial to apply software testing and program analysis techniques to ensure the robustness, fairness, explainability, and reliability of AI models, especially when AI is applied into safety- and security-critical applications.

The AISTA workshop aims to create an opportunity for the researchers to discuss their research, share recent ideas, and present new perspectives at the intersection of AI and Software Testing/Analysis, i.e., AI for Software Testing/Analysis and Software Testing/Analysis for AI. The workshop will consist of invited talks and presentations based on research paper submissions.

For more information please consult https://ai-sta.github.io/aista21/

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Conference Day
Mon 12 Jul

Displayed time zone: Brussels, Copenhagen, Madrid, Paris change

09:00 - 11:50
Main SessionAISTA at AISTA
Chair(s): Xiaofei XieNanyang Technological University, Shuai WangHong Kong University of Science and Technology, Lei MaUniversity of Alberta
Towards Automated Debugging: A Trace Travelling Oriented and AI-based Approach
Yun LinNational University of Singapore
NerdBug: Automated Bug Detection in Neural Networks
Foad JafarinejadTechnical University of Darmstadt, Krishna NarasimhanTU Darmstadt, Germany, Mira MeziniTU Darmstadt, Germany
Automated Cell Header Generator for Jupyter Notebooks
Impact of Programming Languages on Machine Learning Bugs
Sebastian SztwiertniaTechnical University of Darmstadt, Maximilian GrübelTechnical University of Darmstadt, Amine ChouchaneTechnical University of Darmstadt, Daniel SokolowskiTechnical University of Darmstadt, Germany, Krishna NarasimhanTU Darmstadt, Germany, Mira MeziniTU Darmstadt, Germany
Link to publication DOI Pre-print
On the use of Evolutionary Algorithms for Test Case Prioritization in Regression Testing considering Requirements Dependencies
Andreea VescanBabes-Bolyai University, Camelia Chisalita-CretuBabes-Bolyai University Cluj-Napoca, Camelia SerbanDepartment of Computer Science, Babes-Bolyai University, Laura DiosanBabes-Bolyai University
Panel discussion

AISTA invites the following submissions:

  • Full papers (up to 8 pages; including references): Original, unpublished results that are related to the topics of AISTA.

  • New idea papers (up to 4 pages; including references): New ideas supported by initial evidence.

  • Fast abstract papers (up to 2 pages; including references)

We plan to organize AISTA as an one-day workshop, including a half-day keynote session and a half-day presentation of accepted papers. Note that all papers, including full papers, new idea papers, and fast abstract papers, will receive a full presentation and discussion slot.

AISTA focuses on the intersection of AI and Software Testing/Analysis. We welcome (but not limited to) the following topics:

  • AI for program static and dynamic analysis
  • AI for software testing
  • AI for other software engineering topics like optimization or code comprehension
  • Apply software testing techniques to AI models
  • Apply program static or dynamic analysis techniques to AI models
  • Testing/analysis AI infrastructures
  • Empirical study of related topics


Papers must conform to the ACM conference format and in PDF. Papers will be reviewed in a double-blinded process. Papers can be submitted via EasyChair (https://easychair.org/conferences/?conf=aista21). In particular, the following LaTeX code should be placed at the start of the LaTeX document.


Proceedings of AISTA 2021 will be available through the ACM DL and indexed in DBLP.