Research | Software Quality and Measurement (SQMLab)/Web Engineering

Description: The SQMLab's activities are mainly aimed at studying methods and tools for monitoring, controlling and improving the quality of software products and projects. The focus is on the measurement since "You cannot control what you cannot measure" (T. DeMarco). Software measurement is the basis for many activities such as effort and cost estimation, evaluation of the resources, activity planning, evaluation and quality control. The measurement of ICT processes and services is equally important. The scientific investigation of the laboratory aims at identifying and evaluating metrics and techniques to be used during the software development and maintenance process, also in order to understand its applicability and limitations. A crucial aspect of the performed investigations is represented by the experimental part (surveys, controlled experiments, case studies).

Main research interests:

• Definition and analysis of functional size measurement methods to be employed in the early phases of software development process in order to obtain “rapid” and “early” measurement information.
• Application and analysis of Functional Size Measurement Methods (1st generation methods, like Function Points, 2nd generation methods, like COSMIC) in emerging software development contexts (e.g., mobile app development)
• Analysis of Machine Learning techniques for building effort estimation models, based on the use of dimensional measures and functional size measures
• Using “search-based” approaches in the context of software engineering for building estimation models to be used for managing software projects, for identifying test cases for testing
• Software modeling as support for developing software systems as well as to improve the quality and the effectiveness of method and models to be employed for maintaining software systems.
• Design pattern recovery from object-oriented source code to support comprehension and maintenance of software systems
• Construction and validation of prediction models built from data collected on software products and software processes, or as a result of experiments

Research collaborations: the members of SQMLab have scientific collaborations with the following researchers (the results of the collaborations have been published on journal and conference papers):

• Emilia Mendes, Blekinge Institute of Technology, Karlskrona: Sweden. Topic: Software effort estimation
• Silvia Abrahão e Emilio Insfran, Università Politecnica de Valencia, Spain. Topic: Language and tool investigation for modeling and developing/maintaining software.
• Marcela Genero Bocco e Josè Antonio Lemus-Cruz, Università Castilla-La Mancha, Spain. Topic: Language and tool investigation for modeling and developing/maintaining software.
• Cigdem Gencel, Blekinge Institute of Technology, School of Computing, Università di Bolzano. Topic: Software effort estimation
• Luigi Buglione, École de Technologie Supérieure (ETS) – Università di Québec, Canada. Topic: Software effort estimation
• Andrian Marcus, Wayne State University, USA. Topic: Bug prediction techniques.
• Tim Menzies, West Virginia University, USA. Topic: Bug prediction techniques.
• Anna Corazza, Sergio Di Martino, Università degli Studi di Napoli “Federico II”. Software effort estimation
• Giuseppe Scanniello, Università della Basilicata. Topic: Software maintenance.
• Federica Sarro, University College London, Department of Computer Science. Topic: Functional size measurement, Software effort estimation, Search-Based Software Engineering, App mining
• Rocco Oliveto, Università del Molise. Topic: Software maintenance.
• Gabriele Bavota, Università della Svizzera italiana (USI), Topic: Software maintenance.
• Filippo Ricca, Università di Genova. Topic: Software maintenance.
• Marco Torchiano, Politecnico di Torino. Topic: Software maintenance.
• Gabriella Dodero, - Libera Università di Bolzano. Topic: Software maintenance
• Luigi Lavazza - Università degli Studi dell'Insubria. Topic: Functional size measurement