[AISWorld] [CFP] Special Issue on Causal Modeling and Inference in SE, Information and Software Technology (IST)

[WASHIZAKI Hironori]

===
*Call for Papers*

Special Issue of the Journal of Information and Software Technology (IST) on: 

Application of causal modeling and inference methods in software engineering: 
Approaches, Challenges, State-of-the-Art and Prospects

https://www.sciencedirect.com/journal/information-and-software-technology/
https://www.sciencedirect.com/journal/information-and-software-technology/about/call-for-papers#application-of-causal-modeling-and-inference-methods-in-software-engineering-approaches-challenges-state-of-the-art-and-prospects

Submission Deadline: 30th September 2023

Guest editors:
Adam Trendowicz, Fraunhofer Institute for Experimental Software Engineering, Germany

Hironori Washizaki, Waseda University, Japan

Gregor Gössler, Inria Grenoble Rhône-Alpes, France

Julien Siebert, Fraunhofer Institute for Experimental Software Engineering, Germany

Martin Shepperd, Brunel University, United Kingdom

Michael Kläs, Fraunhofer Institute for Experimental Software Engineering, Germany

Special issue information:

This special issue provides an opportunity for researchers and practitioners to present theory, techniques, and applications related to the use of causal modelling and causal inference methods in software engineering. While much attention in the software engineering community has been given to the investigations based on correlation analysis techniques, advances in the field of causal modelling and inference remain to a large extent unexplored.

The recent growth and development of data science practices and artificial intelligence has brought more attention to the issue of causality. The gold standard for estimating causal effects is to conduct controlled experiments with randomized controlled trials. However, when this is not possible (e.g., for practical or ethical reasons), causal effects must be estimated from observational data (i.e., data that were not generated in randomized controlled trials) or judged by subject matter experts. A major challenge with observational data, assuming all relevant variables have been captured, is that they may contain confounding correlations that can bias the estimation of causal effects. The field of causal inference has developed methods to reduce the impact of confounding factors and separate spurious correlations from causal effects.

In the last decade, causal inference methods have begun to be applied in the field of software engineering. This is because (i) questions of causality are fundamental to obtaining actionable results, (ii) causal inference methods provide a way to evaluate the design of an empirical study in a principled way, such as deciding which elements to condition on and which would introduce bias, and (iii) these approaches also allow latent and unobserved variables to be handled in an elegant way.

In addition, the use of causality within the software engineering process can be used to help the user locate defects, explain failures, enforce liability requirements, or even help resolve liability issues in software engineering.

In this special issue, we intend to publish work studying causality and causal analysis methods in software engineering. We intend to keep the scope of the application use cases (i.e., software engineering) as broad as possible (for example, using the Software Engineering Body of Knowledge (SWEBOK) as a frame of reference). However, we will limit the type of analysis methods used to causal discovery and inference. We are particularly interested in methods that make explicit use of a graphical causal model. The types of work expected include (not limited to) proof of concept, benchmarks, empirical studies, lessons learned reports, literature reviews, etc.

The special issue aims to collect primary and secondary studies on the adaptation, application and empirical evaluation of causal discovery and inference methods and techniques, including related methodologies, challenges and future prospects.

Papers may focus on, but are not restricted to, the following themes:

* Causal analysis using observational software engineering data.
* Causal analysis based on judgment of software engineering human experts.
* Hybrid causal analysis methods, which combine the above-mentioned approaches.
* Causal structure discovery analysis.
* Counterfactual analysis.
* Causal debugging and explanations in software engineering.
* Types of studies presented in the papers may include, but are not restricted to the following:

Introduction of new or adaptation of existing causal methods in the context of software engineering, incl. critical reflection on their applicability, at best based on a systematic empirical evaluation.
Recent adaptations and practical applications of causal methods for software engineering purposes.
Empirical evaluation of causal methods in the software engineering context, particularly in case studies and experiments.
State-of-the-art surveys on causal discovery and inference in software engineering.
Experience reports on practical applications of causal methods in software engineering.
Manuscript submission information:

Journal of Information and Software Technology special issue papers will go through no more than two full rounds of peer review. Submissions to the Journal of Information and Software Technology special issue should follow the regular rules for research paper submissions.

Submission guidelines: https://www.elsevier.com/journals/information-and-software-technology/0950-5849/guide-for-authors 

Benefits of publishing in a special issue: https://www.elsevier.com/authors/submit-your-paper/special-issues 

Submission system: https://www.editorialmanager.com/infsof/default2.aspx 

Journal homepage: https://www.sciencedirect.com/journal/information-and-software-technology 

Please note that IST now publishes Special Issues using the following 'Virtual Special Issue' workflow.

Manuscripts are submitted to EM through the Special Issue portal and go through peer review as usual.
Once a manuscript is accepted it goes into production, and it is simultaneously published in the current regular issue and pulled into the online Special Issue.
Articles from a Special Issue will appear in different regular issues of the journal, although they will be clearly marked and branded as Special Issue articles.
==