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ICSE 2023
Sun 14 - Sat 20 May 2023 Melbourne, Australia

DeepTest is a high-quality workshop for research at the intersection of Machine Learning (ML) and software engineering (SE). ML is widely adopted in modern software systems, including safety-critical domains such as autonomous cars, medical diagnosis, and aircraft collision avoidance systems. Thus, it is crucial to rigorously test such applications to ensure high dependability. However, standard notions of software quality and reliability become irrelevant when considering ML systems, due to their non-deterministic nature and the lack of a transparent understanding of the models’ semantics. ML is also expected to revolutionize software development. Indeed, ML is being applied for devising novel program analysis and software testing techniques related to malware detection, fuzzy testing, bug-finding, and type-checking.

The workshop will combine academia and industry in a quest for well-founded practical solutions. The aim is to bring together an international group of researchers and practitioners with both ML and SE backgrounds to discuss their research, share datasets, and generally help the field build momentum. The workshop will consist of invited talks, presentations based on research paper submissions, and one or more panel discussions, where all participants are invited to share their insights and ideas.

Call for Papers

NOTICE (09 Jan 2023): Only those who submitted by the original deadline (January 13, 2023) will be given one more week to update their submitted papers. Please note that no new submissions will be accepted after the original deadline.

DeepTest is an interdisciplinary workshop targeting research at the intersection of software engineering and deep learning. This workshop will explore issues related to:

  • Deep Learning applied to Software Engineering (DL4SE)
  • Software Engineering applied to Deep Learning (SE4DL)

Although the main focus is on Deep Learning, we also encourage submissions that are more broadly related to Machine Learning, as well as submissions related to (Deep) Reinforcement Learning.

Topics of Interest

We welcome submissions introducing technology (i.e., frameworks, libraries, program analyses and tool evaluation) for testing DL-based applications, and DL-based solutions to solve open research problems (e.g., what is a bug in a DL/RL model). Relevant topics include, but are not limited to:

  • High-quality benchmarks for evaluating DL/RL approaches
  • Surveys and case studies using DL/RL technology
  • Techniques to aid interpretable DL/RL techniques
  • Techniques to improve the design of reliable DL/RL models
  • DL/RL-aided software development approaches
  • DL/RL for fault prediction, localization and repair
  • Fuzzing DL/RL systems
  • Metamorphic testing as software quality assurance
  • Fault Localization and Anomaly Detection
  • Use of DL for analyzing natural language-like artefacts such as code, or user reviews
  • DL/RL techniques to support automated software testing
  • DL/RL to aid program comprehension, program transformation, and program generation
  • Safety and security of DL/RL based systems
  • New approaches to estimate and measure uncertainty in DL/RL models

Types of Submissions

We accept two types of submissions:

  • Full research papers up to 8-page papers (including references) describing original and unpublished results related to the workshop topics;
  • Short papers up to 4-page papers (including references) describing both preliminary work, new insights in previous work, or demonstrations of testing-related tools and prototypes.

All submissions must conform to the ICSE 2023 formatting instructions. All submissions must be in PDF. The page limit is strict.

Submissions must conform to the IEEE formatting instructions IEEE Conference Proceedings Formatting Guidelines (title in 24pt font and full text in 10pt type, LaTeX users must use \documentclass[10pt,conference]{IEEEtran} without including the compsoc or compsocconf options).

DeepTest 2023 will employ a double-blind review process. Thus, no submission may reveal its authors’ identities. The authors must make every effort to honor the double-blind review process. In particular, the authors’ names must be omitted from the submission, and references to their prior work should be in the third person.

The official publication date of accepted papers is the date the proceedings are made available in the ACM or IEEE Digital Libraries. This date may be up to two weeks prior to the first day of ICSE 2023. The official publication date affects the deadline for any patent filings related to published work. Purchases of additional pages in the proceedings is not allowed.

If you have any questions or wonder whether your submission is in scope, please do not hesitate to contact the organizers.

Important Dates

  • Paper Submission: January 13, 2023 (AoE) (NEW: You can update your submitted papers until January 20 only if the initial submission is made before January 13)
  • Acceptance Notification: February 24, 2023 (AoE)
  • Camera Ready: March 17, 2023 (AoE)
  • Workshop Date: May 15, 2023

Submission System


Special Issue

Authors of DeepTest 2023 papers are encouraged to submit revised, extended versions of their manuscripts for the special issue in the Empirical Software Engineering (EMSE) journal, edited by Springer (details will follow). The call is also open to non-DeepTest 2023 authors.


  • Matteo Biagiola, Università della Svizzera italiana, Switzerland
  • Nicolás Cardozo, Universidad de los Andes, Colombia
  • Foutse Khomh, Polytechnique Montréal, Canada
  • Vincenzo Riccio, Università della Svizzera italiana, Switzerland
  • Donghwan Shin, University of Sheffield, United Kingdom
  • Andrea Stocco, Università della Svizzera italiana, Switzerland

Questions? Use the DeepTest contact form.