Effort Estimation in Software Development Projects Using Supervised Machine Learning: A Comparative Analysis with ISBSG Data
by Jesús Alberto Getial Barragán and Silvio Ricardo Timarán Pereira
Effort estimation in software development projects is critical for the effective planning of resources, costs and execution times. However, obtaining accurate estimations can be challenging. Inaccuracies may result in cost overruns or the failure to successfully complete a project. To address this challenge, different strategies have  been developed. Machine learning models have demonstrated effectiveness in capturing patterns from historical software project data, creating estimation models, thereby improving accuracy.
This presentation analyses a machine learning model, that uses ISBSG Data for effort estimation. It accuracy is discussed along with the future. Such machine learning models support the development of predictive tools tailored to individual organizational contexts.
Watch presentation on YouTube
About Jesús Alberto Getial Barragán
Jesús Alberto Getial Barragán holds an honors degree in Electronic Engineering and he is currently a scholarship recipient and candidate in the Master’s program in Systems and Computing Engineering at the University of Nariño in Colombia. He is an active member of the GRIAS Research Group. His academic and professional interests include machine learning, business intelligence, robotics, and software architecture and development. With over seven years of experience as a Senior Software Engineer, he has worked as a Full-Stack Developer and Data Analyst for companies based in the United States, India, and Latin America.. See LinkedIn profile
About Silvio Ricardo Timarán Pereira
Silvio Ricardo Timarán-Pereira holds a Ph.D. in Engineering with an emphasis on Computer Science, a Master of Science in Engineering, and a specialization in Educational Multimedia. He is also a Systems and Computer Engineer. Currently, he serves as a Full Professor in the Department of Systems at the Faculty of Engineering at the University of Nariño in Colombia, and he is the Director of the GRIAS Research Group. His research focuses on data mining, machine learning, and business intelligence, fields in which he has authored numerous articles published in indexed journals and has presented his work at various international conferences.