Bug Prediction Metrics Based Decision Support for Preventive Software Maintenance

Abstract

There exist a number of large legacy systems that still undergo continuous maintenance and enhancement. Due to the sheer size and complexity of the software systems and limited resources, managers are confronted with crucial decisions regarding allocation and training of new engineers, intelligent allocation of testing personnel, assessment of release readiness of the software and so on. While the area of bug prediction by mining software repositories holds promise, and is a worthwhile endeavor, the current state of the art techniques are not accurate enough in predicting bugs and hence are of limited usefulness to managers. So instead of predicting files as buggy or not we take a different viewpoint and focus on providing decision support for managers. In this paper we present a set of metrics to guide the managers in taking these decisions. These metrics are evaluated using 4 open source systems and 2 proprietary systems.

A Hybrid eBusiness Software Metrics Framework for Decision Making in Cloud Computing Environment

“…Maskeri et al presented a set of metrics by mining previous software repositories to guide managers for making decisions to predict future software bugs. The experiment shows that their method is efficient for bug prediction [15].…”

Decision Making Framework in e-Business Cloud Environment Using Software Metrics

“…The implementation of software metrics using supporting tools and methods [9] several current metrics able to fit the metric-driven software process [10] GQM represented the Software life cycle deals all the type of software metrics from any stages like store, model [11] software metrics used to estimate and predict the future projects such as techniques, risk and cost. Classification of techniques used to predict the risk in different stages [12] comparing the cost and project plans from earlier projects to make plan for future projects evaluating the software performance using intelligent techniques [13] patters are most important one to compare the similarity of a different patters with code metrics as predictors for code analysis in future projects [14] Based on the preventive software its shows the efficient of bug prediction [15] Software metrics grouped in several categories they are statistical, machine learning [16],neural networks [17] [18] and decision making methods [19]this models working based on relationship between input and output data.…”