#SOFTWARE REVIEWS DATACROW SOFTWARE#

#SOFTWARE REVIEWS DATACROW SOFTWARE#

Detection of such classes earlier in life cycle model of software can reduce maintenance costs as because if an error is detected early in a product, it would require lesser amount of resources to correct that error. By focusing on weak change prone classes utilization of resources can be done in a better way. As the software evolves it demands more rigorous testing so that good quality software can be developed with less changes and defects. probability with which class will modify or not needs to be evaluated as it can help in reducing maintenance cost and testing. Malhotra and Khanna (2013) identified maintenance cost incurs 40-70% of entire cost of software. Maintenance phase is considered as one of the costly and significant phases of software.

Predicting such changes can be useful as such evaluations can be utilized to forecast changes from one release to next. Changes if predicted during earlier stages of life cycle, can help a developer in efficiently allocating project’s resources by properly allocating the appropriate resources to weaker change prone class, so that such type of classes can be maintained properly and tested rigorously. Weak classes of any software can be predicted using these quality attributes. Research has also been carried out to find the association between fault prone classes and object-oriented metrics. Developing and maintaining software requires resources such as development time, cost to build and effort required. This rapidly changing software demand has resulted in significant increase of effort from development to testing phase in software life cycle. Software changes to incorporate new features or to remove errors. Software industry is expanding manifolds day by day. Koru and Liu (2007) proved change prone classes as a significant peripheral quality attribute that signifies degree of alterations in a class through various versions of software. Finding software change proneness is a significant and essential activity for improving software feature and reducing maintenance effort formerly the software is installed in real world. The unending growing complexity and dependency has led to a rise in demand of high quality software that can be maintained at cheaper costs. The proposed metrics contribute to the accurate prediction of change-prone classes. Higher values of AUC indicate the prediction model gives significant accurate results.

For evaluating the performance of the prediction model, the authors used Sensitivity, Specificity, and ROC Curve. In this article, some new metrics such as execution time, frequency, run time information, popularity and class dependency are proposed which can help in prediction of change prone classes. Earlier, researchers have used various techniques such as statistical methods for the prediction of change-prone classes. Change prediction models can detect the probability of change in a class earlier in the software life cycle which would result in better effort allocation, more rigorous testing and easier maintenance of any software. This article aims at the association between changes and object-oriented metrics using different versions of open source software. In order to compete with pace of new technology, changes in software are inevitable. AbstractIn today's world, the heart of modern technology is software.

Software reviews datacrow