A new failures model for software application development process using ensemble method
To develop software projects, one of the major demands is high system functionality in order to get over the complex system requirements. The ability to predict the probable software system failures early can help organizations in making decisions about possible solutions and improvements including...
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| Format: | Thesis Book |
| Language: | English |
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| Summary: | To develop software projects, one of the major demands is high system functionality in order to get over the complex system requirements. The ability to predict the probable software system failures early can help organizations in making decisions about possible solutions and improvements including engaging new experts and changing project development plan. Inaccurate failure analysis could lead the software project toward undesirable events. Therefore, to overcome this problem, this research focuses on early software project failure prediction using different machine learning methods. Furthermore, ensemble techniques are used to improve the model classification results, as different classification abilities of their base single classifiers enable the proposed algorithms to capture different characteristics of the training data and produce more reliable and accurate classification This research aims to determine the factors behind software project failures, in order to develop predictive models using ensemble methods that use dataset constructed using historical data collected from past software projects reports. A framework for software project failure prediction is proposed to obtain the expected software project failure as well as project's failure probability. To obtain reliable and accurate software failure prediction, we used an evidence- based approach which depends on gathering information about successful and failed software project from available resources such as reports, case studies and surveys. The first step of developing the classification models is structuring a data set. Then, the constructed data is partitioned into training and testing sets. The training data is used in different ways to train the models while the testing data is used to measure their prediction performance. After developing and testing the model, it can be deployed and used during actual software projects development process to predict the future outcomes of these projects. Initially, the predictive model is implemented using six of an existing machine learning techniques in an attempt to achieve diversity. Furthermore, the research proposed two machine learning ensemble approaches to enhance the performance of the predictive models. The first proposed model uses the results of the six implemented models to develop new ensemble model based on majority voting. The second model proposes new approach that gives the higher rank (weight) to the base classifier that showed higher performance in predicting the most difficult data than other classifiers in the ensemble. Finally, the performance of the developed models is compared using different measures such as confusion matrix, accuracy and sensitivity. The results show that using the proposed weighted ensemble method for predicting software project failures has better performance than other methods in terms of accuracy (90%), sensitivity (92%) and other performance measures. However, the other developed models appear fairly accurate and produce acceptable performance results. This research began by identifying factors behind software project success and failures, in order to develop accurate failure predictive models using different methods. This research contributes to the field of software system development as it extracts software project failure dataset and as it develops software project failure classification models that can be generally applied on any software project during any phase of software development process. Most of previously proposed classification models and tools were developed and verified based on certain case studies. Furthermore, the research proposes a new ensemble machine learning models to improve the failure prediction performance. Finally, the research suggests that the proposed models can be integrated within the development process of the software system. This integration is realized through developing evaluation tool to generate the failure probability of the project. |
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| Item Description: | x |
| Physical Description: | xx, 218 pages: illustrations(somecolour); 31 cm. |
| Bibliography: | Includes bibliographical references (leaves 198-216) |
| ISBN: | x |