Evolutionary Methods and Machine Learning in Software Engineering, Testing and SE Repositories (SEBASENet@CEC'2017)

Special Session at IEEE Congress on Evolutionary Computation (CEC'2017) Donostia - San Sebastián, Spain, June 5-8, 2017

"IEEE CEC 2017 is a world-class conference that aims to bring together researchers and practitioners in the field of evolutionary computation and computational intelligence from all around the globe".

Aims and Scope

This CEC'2017 special session aims to bring together both theoretical developments and applications of Computational Intelligence to software engineering (SE), i.e.,the management, design, the development, operation, maintenance, and testing of software. All bio-inspired computational paradigms and machine learning techniques are welcome, such as Genetic and Evolutionary Computation, including Multi-Objective Approaches, Fuzzy Logic, Intelligent Agent Systems, Neural Networks, Cellular Automata, Artificial Immune Systems, Swarm Intelligence, and others, including machine learning techiques.

Currently, an increasing number of researchers from the SE discipline are focusing on applying computational intelligence techniques such as meta-heuristics (known as Search based software engineering -SBSE-), data mining or statistics to their research. Problems such as planning and decision making in software engineering, arrangements of modules, finding patterns of defective modules, cost and effort estimation, testing and test case generation, debugging and fault localisation, knowledge extraction, etc. can be reformulated or addressed using a set of techniques which includes searching and optimization techniques, data mining and machine learning, simulation, process mining, etc. These techniques, already used extensively in other areas, are incrementally being applied in software engineering.

There is a large number of decisions during the development and maintenance of any software system. Evolutionary methods and data mining can help with the decision making process based on the information available (e.g., estimation and planning of projects) or with the generation of artifacts (e.g., test case generation). Furthermore, modern development environments (IDEs, Issue Tracking Systems and Configuration Management Systems) allow us to collect large amount of data during the executing of a project for real-time decisions as well as application repositories (AppStore, Google Play) containing huge amount of valuable information that can be exploited.


Topics of interest include:

  • Search-based Software Engineering
  • Requirements engineering
  • Automated design and development of software
  • Genetic improvement of software
  • Software maintenance and self-repair
  • Software effort estimation and fault prediction
  • Software reliability, testing and security with data-mining or meta-heuristic techniques
  • Project management, planning and scheduling
  • Studies, applications and tools to extract information from software repositories
  • Dealing with data problems in software repositories (noise, imbalance, outliers, etc.) when applying ML or meta-heuristics
  • Process mining
  • Mining mobile application repositories (AppStore and Google Play)
  • Tools based on evolutionary or ML methods in SE
  • Real world applications of the above

Organizers and PC Members

Submission Guidelines

Following the CEC'2017 guidelines, all the papers have to be submitted electronically through the conference Web application.

Important Dates

  • Submission deadline: January 30, 2017 (Extended)
  • Notification of acceptance: February 26, 2017

Accepted papers

Final program

Y. Li, T. Yue, S. Ali, L. Zhang. A multi-objective and cost-aware optimization of requirements assignment for review" Icono textoweb.png
P. Delgado-Pérez, I. Medina-Bulo, M. Nuñez. 'Using Evolutionary Mutation Testing to Improve the Quality of Test Suites Icono textoweb.png
A. Arrieta, S. Wang, U. Markiegi, G. Sagardui, L. Etxeberria. 'Search-Based Test Case Generation for Cyber-Physical Systems Icono textoweb.png
A. Ramírez, J. R. Romero, S. Ventura. On the Effect of Local Search in the Multi-objective Evolutionary Discovery of Software Architectures Icono textoweb.png
I. Ibarguren, J. Pérez, J. Muguerza, D. Rodríguez, R. Harrison. The Consolidated Tree Construction Algorithm in Imbalanced Defect Prediction Datasets Icono textoweb.png
A. Ghannem, M. S. Hamdi, M. Kessentini, H. H. Ammar. Search-Based Requirements Traceability Recovery: A Multi-Objective Approach Icono textoweb.png


SEBASENet - Red de Excelencia en Ingeniería de Software basada en Búsqueda (Spanish Search Based Software Engineering Network)