Data-driven Predictions of Software Attributes in Future Products

Abstract

Productions of software systems are increasing rapidly. Software managers have to make estimation calculation and assign specific time period to the different phases of software development process when a project is proposed. The use of expert judgement and intuition in software estimations are mostly inaccurate due to a lack of systematic estimation process. Due to this the company suffers as well as the customer. Our aim is to make a tool that will help the software development companies in data-driven predictions of software attributes (e.g., defects) while using already collected data during development of previous projects e.g., source lines of code (SLOC), defects, function points. The tool will use machine learning and data mining techniques for data-driven prediction and it will automate standard estimation methods of COCOMO, IFPUG and COSMIC. It will help the managers to perform estimations using different graphical user interfaces in the developed software. The tool will also provide a mechanism to use publicly available datasets (e.g., ISBSG and NASA) to build machine learning models that will also help the managers to predict attributes in similar software projects.