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Empirical Software Engineering - Call for Papers: Machine Learning Techniques for Software Quality Evaluation

A Call for papers to a special issue of the Empirical Software Engineering (EMSE) journal.

Editors of the special issue

  • Bibi Stamatia (main contact), University of Western Macedonia, Greece, [sbibi@uowm.gr] (mailto: sbibi@uowm.gr)
  • Maxime Cordy, University of Luxembourg, Luxembourg, [maxime.cordy@uni.lu] (mailto: maxime.cordy@uni.lu)
  • Bowen Xu, Singapore Management University, Singapore, bowenxu.2017@smu.edu.sg
  • Xiaofei Xie, Singapore Management University, Singapore, [xiaofei.xfxie@gmail.com] (mailto:xiaofei.xfxie@gmail.com)

Description of the special issue

The assessment of software quality is one of the most multifaceted (e.g., structural, product, and quality) and subjective aspects of software engineering (since in many cases it is substantially based on expert judgment). Such assessments can be performed at almost all the phases of software development (from project inception to maintenance) and at different levels of granularity (from source code to architecture).

However, human judgment is inherently biased by implicit, subjective criteria applied to the evaluation process, and its economical effectiveness is limited compared to automated or semi-automated approaches. To this end, researchers are still looking for new and more effective methods for assessing various qualitative characteristics of software systems and the related processes.

In recent years, we have been observing a rising interest in adopting various approaches to exploiting machine learning and automated decision-making processes in several areas of software engineering. The machine learning models and algorithms help to reduce effort and risk related to human judgment in favor of automated systems, which are able to make informed decisions based on available data and evaluated with objective criteria. Therefore, the adoption of machine learning techniques seems to be one of the most promising ways to improve software quality evaluation.

Conversely, learning capabilities are increasingly embedded within software, including in critical domains such as automotive and health. For this reason, the application of quality assurance techniques is required to ensure the reliable engineering of software systems based on machine learning. As such, the special issue will invite submissions on new and innovative research results and industrial experience papers in the area of machine learning applications for software quality evaluation.

Submission topics

Submissions could deal with all aspects of the problem, including, but not limited to, the following topics of interest:

  • Application of machine-learning in software quality evaluation
  • Analysis of multi-source, multi-modal software data
  • Supporting the application of software engineering practices through machine learning
  • Knowledge acquisition from software repositories
  • Adoption and validation of machine learning models and
  • Algorithms in software quality
  • Decision support and analysis in software quality
  • Prediction models to support software quality evaluation
  • Validation and verification of systems, learning; item validation and verification of systems based on machine learning
  • Automated machine learning
  • Design of safety-critical learning software
  • Integration of learning systems in software ecosystems
  • Machine learning for software reuse
  • Quality assurance of machine learning techniques when applied to software systems
  • Machine learning engineering for production (MLOPs) deployment in software applications.
  • Machine learning for edge and IoT software development challenges

Schedule

Submission Deadline: April 15, 2023

Submission instructions

Papers should be submitted through the Empirical Software Engineering editorial manager website (http://www.editorialmanager.com/emse/) as follows (1) select "Research Papers" and (2) later on the Additional Information page:

  • answer "Yes" to "Does this paper belong to a special issue?";
  • and select "Machine Learning Techniques for Software Quality Evaluation" for "Please select the issue your manuscript belongs to".

For formatting guidelines as well as submission instructions, please visit http://www.springer.com/computer/swe/journal/10664?detailsPage=pltci_2530593

EMSE encourages open science and reproducible research for this special issue. Please see our [Open Science Initiative] (https://github.com/emsejournal/openscience) for further information.

Submission guidelines

Papers must be prepared in accordance with the Journal guidelines: https://www.springer.com/journal/10664/submission-guidelines (this opens in a new tab)  

Submitted papers should present original, unpublished work, relevant to one of the topics of the Special Issue. All submitted papers will be evaluated on the basis of relevance, significance of contribution, technical quality, scholarship, and quality of presentation, by at least three independent reviewers. It is the policy of the journal that no submission, or substantially overlapping submission, be published or be under review at another journal or conference at any time during the review process. Manuscripts will be subject to a peer reviewing process and must conform to the author guide lines available on the EMSE website at: https://www.springer.com/10664 

Author Resources

Authors are encouraged to submit high-quality, original work that has neither appeared in, nor is under consideration by other journals.  

Springer provides a host of information about publishing in a Springer Journal on our Journal Author Resources (this opens in a new tab) page, including  FAQs (this opens in a new tab),  Tutorials (this opens in a new tab)  along with  Help and Support. (this opens in a new tab)

Other links include:

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