Dr. Andreas L. Symeonidis is an Associate Professor with the Department of Electrical and Computer Engineering at the Aristotle University of Thessaloniki, Greece and the Chief Research Officer at Cyclopt.com. His research interests include Software engineering processes, Model-driven engineering, Software quality and Software analytics, Middleware Robotics and Knowledge extraction from big data repositories. Dr. Symeonidis’ work has been published in over 150 papers, book chapters, and conference publications. He is co-author of the books “Agent Intelligence through Data Mining” (Springer publishing), “Mining Software Engineering Data for Software Reuse” (Springer publishing) and “Practical Machine Learning in R” (Leanpub publishing). He is currently coordinating more than 10 contract R&D projects, while serving occasionally as a R&D project evaluator and reviewer for the European Commission. More here
Improving the modern software engineering lifecycle through Software Analytics
Despite the indisputable advances in software engineering methodologies, tools and approaches, software projects fail often and sometimes they fail hard: bad engineering practices, erroneous requirements elicitation, wrong selection of technologies, poor product quality are some of the main reasons. In order to remedy these issues, one has to first identify, then analyze and finally act upon them in a prompt, yet effective manner; and software analytics aspire to complement the software engineering process and improve the final outcome. Software analytics can be applied to a wide spectrum of data related to software development; analysis can be performed during all software engineering phases (analysis, design, implementation, operation) and against various aspects (performance, usability, maintainability, reuse, etc.). It can be performed in an offline or online mode, and can involve developer, operator and/or user perspectives.
This talk will provide an overview of issues that can be addressed through Software analytics in order to improve the software engineering lifecycle. Various Machine learning/Information retrieval approaches will be discussed, focusing on the different software engineering phases and employing different SE-related data available (text, source code, repository (meta)data, user data).