Abstract-A high prediction accuracy of the students' performance is helpful to identify the low performance students at the beginning of the learning process. Machine learning is used to attain this objective. Machine learning techniques are used to discover models or patterns of data, and it is helpful in the decision-making. The ability to predict performance of students is very crucial in our present education system. We applied Machine learning concepts for this study. The dataset used in our study is taken from the Wolkite university registries office for college of computing and informatics from 2004 up to 2007 E.C with respect to each department. In this study, we have been collected student's transcript data that included their final GPA and their grades in all courses. After pre-processing the data, we applied the machine learning methods, neural networks, Naive Bayesian and Support Vector Machine (SMO). Finally, we built the model for each method, evaluate the performance and compare the results of each model. Using machine learning, the aim was to develop a model which can derive the conclusion on students' academic success.
Software metrics have a direct link with measurement in software engineering. Correct measurement is the prior condition in any engineering fields, and software engineering is not an exception, as the size and complexity of software increases, manual inspection of software becomes a harder task. Most Software Engineers worry about the quality of software, how to measure and enhance its quality. The overall objective of this study was to asses and analysis's software metrics used to measure the software product and process. In this Study, the researcher used a collection of literatures from various electronic databases, available since 2008 to understand and know the software metrics. Finally, in this study, the researcher has been identified software quality is a means of measuring how software is designed and how well the software conforms to that design. Some of the variables that we are looking for software quality are Correctness, Product quality, Scalability, Completeness and Absence of bugs, However the quality standard that was used from one organization is different from others for this reason it is better to apply the software metrics to measure the quality of software and the current most common software metrics tools to reduce the subjectivity of faults during the assessment of software quality. The central contribution of this study is an overview about software metrics that can illustrate us the development in this area, and a critical analysis about the main metrics founded on the various literatures.
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