Artificial Intelligence (AI) is getting more and more popular, being adopted in a large number of applications and technologies we use on a daily basis. A large number of AI-enabled applications are produced by developers without proper training on software quality practices or processes, and in general, lack state-of-the-art software engineering processes.

Guest editors:

Prof. Michael Felderer is an associate professor at the Department of Computer Science at the University of Innsbruck, Austria and a guest professor at the Department of Software Engineering at the Blekinge Institute of Technology, Sweden. In 2014 he was a guest researcher at Lund University, Sweden and in 2015 a guest lecturer at the University of Stuttgart, Germany. His fields of expertise and interest are software quality, empirical software engineering as well as AI and software engineering. Michael Felderer holds a habilitation degree from the University of Innsbruck, co-authored more than 150 publications and received 11 best paper awards. He is an internationally
recognized member of the software engineering research community and supports it as an editorial board member of the journals Information and Software Technology (IST), IET Software and International Journal on Software Tools for Technology Transfer (STTT), organizer of conferences
(e.g., General Chair of PROFES 2017 and SE 20, Program Co-Chair of SEAA 2017, Industry Co-Chair of ESEC/FSE 2019, Program Co-Chair of TechDebt 2020) and regular PC member of premier conferences. Overall he has co-organized more than thirty workshops and conferences. Furthermore, he is also a member of the steering committee of PROFES, RET, TAIC PART, and ISERN. Prof. Felderer is recognized by the Journal of Systems and Software (JSS) as one of the twenty most active established Software Engineering researchers world-wide in the period 2013 to 2020. For more information, visit his website at mfelderer.at. (Michael.Felderer@uibk.ac.at)

Dr. Valentina Lenarduzzi is an assistant professor (tenure track) at University of Oulu (Finland). Her research activities are related to modern software development practices and methodologies, including data analysis in software engineering, software quality, software maintenance and evolution, focusing on Technical Debt as well as code and architectural smells. She got the Ph.D. in Computer Science in 2015 and was a postdoctoral researcher at the Free University of Bozen-Bolzano, (Italy), at the Tampere University (Finland), and at LUT University (Finland). Moreover, she was visiting researcher at the University of Kaiserslautern (TUK) and the Fraunhofer Institute for Experimental Software Engineering IESE (Germany). She served as a program committee member of various international conferences (e.g., ICPC, ICSME, ESEM), and for various international journals (e.g., TSE, EMSE, JSS, IST) in the field of software engineering. She has been program co-chair of OSS 2021 and TechDebt 2022. She was also one of the organizer of the last edition of MaLTeSQuE workshop (2022) collocated with ESEC/FSE. Dr. Lenarduzzi is recognized by the Journal of Systems and Software (JSS) as one of the most active SE researcher in top-quality journals in the period 2013 to 2020. For more information, visit his website at valentinalenarduzzi.it (valentina.lenarduzzi@oulu.fi)

Prof. Fabio Palomba is an assistant professor at the Software Engineering (SeSa) Lab of the University of Salerno. He received the European PhD degree in Management \& Information Technology in 2017. His PhD Thesis was the recipient of the 2017 IEEE Computer Society Best PhD Thesis Award. His research interests include software maintenance and evolution, empirical software engineering, source code quality, and mining software repositories. In 2019 he was the recipient of an SNSF Ambizione grant, one of the most prestigious individual research grants in Europe. He serves and has served as a program committee member of various international conferences (e.g., MSR, ICSME), and as referee for various international journals (e.g., TSE, EMSE) in the field of software engineering. He has been program co-chair of ICPC 2021, industrial track co-chair of SANER 2022, ERA track co-chair of MobileSoft 2022, other than program co-chair of MaLTeSQuE 2018 and 2019. Since 2021 he is Editorial Board Member of the Springer's Empirical Software Engineering Journal (EMSE) - formerly, he was within the Review Board since 2016 - and the e-Informatica Software Engineering Journal (EISEJ). He is Review Board Member and Editorial Board Member of several journals. For his reviewing activities, he was the recipient of ten Distinguished/Outstanding Reviewer Awards. (fpalomba@unisa.it)

Dr. Fabiano Pecorelli is a researcher at Tampere University, Finland. He received a bachelor's and master's degree in computer science from the University of Salerno, Italy. In 2018, he started a Ph.D. at the University of Salerno, under the supervision of Professor Andrea De Lucia. He has already submitted a Ph.D. Thesis about technical debt to be defended in January 2022. His research interests include software code and test code quality, predictive analytics, mining software repositories, software maintenance and evolution, and empirical software engineering. Over the last
years, he has already gained good experience in supervising research assistants as well as other Ph.D. students. For more information, visit his website at fabiano-pecorelli.github.io
(fabiano.pecorelli@tuni.fi)

Special issue information:

An AI-enabled system is a software-based system that comprises AI components besides traditional software components. As any software system, AI-enabled systems require attention to software quality assurance (SQA) in general and code quality in particular.

Current development processes, and in particular agile development models, enable companies to decide on the technologies to adopt in their system in a later stage. Therefore, it is hard to anticipate if a system, or if a data pipeline used to develop AI will produce high-quality models.

The main reason is due to the fact that the AI engineer profession was born very recently, and currently there is a very limited number of training or guidelines on issues (such as code quality or testing) for AI and applications using AI code.

According to preliminary studies, developers' training is one of the biggest lacks in software quality assurance for AI, which usually brings several issues related to low quality of AI code as well as low long-term maintenance. Moreover, the software quality of AI-enabled systems is often poorly tested and of very low quality.

The aim of the special issue is to bring together researchers and practitioners from software quality and AI. As an outcome, we would like to build up a community that will target the new challenges emerging in both domains creating fruitful synergies.

Topics of interest include, but are not limited to:

Best practices and experiences on training non software engineering experts on quality assurance for AI.
Software quality aspect related to integration of AI-based components.
The relationship between software quality and system as well as data quality for AI-based systems.
Techniques and tools on software quality for AI.
Testing AI-based systems.

Important Dates

Paper Submission Deadline: July 1st 2022
Initial Author Notification: October 15th 2022
First Revision Deadline: February 1st 2023
Second Author Notification: May 15th 2023
Acceptance Deadline: November 15th 2023
Production Deadline: December 15th 2023

Editors in chief

M.R. Mousavi and A. De Lucia

Guest editors

Michael Felderer, University of Innsbruck, Austria

Valentina Lenarduzzi, University of Oulu, Finland

Fabio Palomba, University of Salerno, Italy

Fabiano Pecorelli, Tampere University, Finland

Decisions:

The guest editors together with the Editors-in-Chief will make the final decision

Manuscript submission information:

Submission guidelines

All submitted papers will undergo a rigorous peer-review process and should adhere to the general principles of the Science of Computer Programming articles. The submission system will be open around one week before the first paper comes in. When submitting your manuscript please select the article type “VSI: SQA4AI”. Please submit your manuscript before the submission deadline. All submissions deemed suitable to be sent for peer review will be reviewed by at least two independent reviewers. Once your manuscript is accepted, it will go into production, and will be simultaneously published in the current regular issue and pulled into the online Special Issue. Articles from this Special Issue will appear in different regular issues of the journal, though they will be clearly marked and branded as Special Issue articles. Submissions have to be prepared according to the Guide for Authors in http://ees.elsevier.com/scico. Submitted papers must be original, must not have been previously published or be under consideration for publication elsewhere. In case a paper has been already presented at a conference, it should be extended by at least 30% new material, before submitted for this special issue. Authors must provide any previously published material relevant to their submission and describe the additions made.