A Novel Machine Learning Approach for Bug Prediction

AbstractWith the growing complexities of the software, the number of potential bugs is also increasing rapidly. These bugs hinder the rapid software development cycle. Bugs, if left unresolved, might cause problems in the long run. Also, without any prior knowledge about the location and the number of bugs, managers may not be able to allocate resources in an efficient way. In order to overcome this problem, researchers have devised numerous bug prediction approaches so far. The problem with the existing models is that the researchers have not been able to arrive at an optimized set of metrics. So, in this paper, we make an attempt to select the minimal number of best performing metrics, thereby keeping the model both simple and accurate at the same time. Most of the bug prediction models use regression for prediction and since regression is a technique to best approximate the training data set, the approximations don’t always fit well with the test data set. Keeping this in mind, we propose an algorithm to predict the bug proneness index using marginal R square values. Though regressions are performed as intermediary steps in this algorithm, the underlying logic is different in nature when compared with the models using regressions alone

Enhancing Software Project Outcomes: Using Machine Learning and Open Source Data to Employ Software Project Performance Determinants

Many factors can influence the ongoing management and execution of technology projects. Some of these elements are known a priori during the project planning phase. Others require real-time data gathering and analysis throughout the lifetime of a project. These real-time project data elements are often neglected, misclassified, or otherwise misinterpreted during the project execution phase resulting in increased risk of delays, quality issues, and missed business opportunities. The overarching motivation for this research endeavor is to offer reliable improvements in software technology management and delivery. The primary purpose is to discover and analyze the impact, role, and level of influence of various project related data on the ongoing management of technology projects. The study leverages open source data regarding software performance attributes. The goal is to temper the subjectivity currently used by project managers (PMs) with quantifiable measures when assessing project execution progress. Modern-day PMs who manage software development projects are charged with an arduous task. Often, they obtain their inputs from technical leads who tend to be significantly more technical. When assessing software projects, PMs perform their role subject to the limitations of their capabilities and competencies. PMs are required to contend with the stresses of the business environment, the policies, and procedures dictated by their organizations, and resource constraints. The second purpose of this research study is to propose methods by which conventional project assessment processes can be enhanced using quantitative methods that utilize real-time project execution data. Transferability of academic research to industry application is specifically addressed vis-à-vis a delivery framework to provide meaningful data to industry practitioners