Software defect prediction framework based on hybrid metaheuristic optimization methods

A software defect is an error, failure, or fault in a software that produces an incorrect or unexpected result. Software defects are expensive in quality and cost. The accurate prediction of defect‐prone software modules certainly assist testing effort, reduce costs and improve the quality of software. The classification algorithm is a popular machine learning approach for software defect prediction. Unfortunately, software defect prediction remains a largely unsolved problem. As the first problem, the comparison and benchmarking results of the defect prediction using machine learning classifiers indicate that, the poor accuracy level is dominant and no particular classifiers perform best for all the datasets. There are two main problems that affect classification performance in software defect prediction: noisy attributes and imbalanced class distribution of datasets, and difficulty of selecting optimal parameters of the classifiers. In this study, a software defect prediction framework that combines metaheuristic optimization methods for feature selection and parameter optimization, with meta learning methods for solving imbalanced class problem on datasets, which aims to improve the accuracy of classification models has been proposed. The proposed framework and models that are are considered to be the specific research contributions of this thesis are: 1) a comparison framework of classification models for software defect prediction known as CF-SDP, 2) a hybrid genetic algorithm based feature selection and bagging technique for software defect prediction known as GAFS+B, 3) a hybrid particle swarm optimization based feature selection and bagging technique for software defect prediction known as PSOFS+B, and 4) a hybrid genetic algorithm based neural network parameter optimization and bagging technique for software defect prediction, known as NN-GAPO+B. For the purpose of this study, ten classification algorithms have been selected. The selection aims at achieving a balance between established classification algorithms used in software defect prediction. The proposed framework and methods are evaluated using the state-of-the-art datasets from the NASA metric data repository. The results indicated that the proposed methods (GAFS+B, PSOFS+B and NN-GAPO+B) makes an impressive improvement in the performance of software defect prediction. GAFS+B and PSOFS+B significantly affected on the performance of the class imbalance suffered classifiers, such as C4.5 and CART. GAFS+B and PSOFS+B also outperformed the existing software defect prediction frameworks in most datasets. Based on the conducted experiments, logistic regression performs best in most of the NASA MDP datasets, without or with feature selection method. The proposed methods also generated the selected relevant features in software defect prediction. The top ten most relevant features in software defect prediction include branch count metrics, decision density, halstead level metric of a module, number of operands contained in a module, maintenance severity, number of blank LOC, halstead volume, number of unique operands contained in a module, total number of LOC and design density

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Sistem e-learning berbasis model motivasi komunitas

e-Learning system is a required solution in education at this globalization era. The existence of e-Learning with information tecnology support bring the transformation from conventional education process into digital form, both content and system perspective. However, recently e-Learning industry is experiencing of crisis, causing to failure and lack of e-Learning implementation in various sector in the world. Failure is especially caused by limited number of user and the lack of motivation to finish eLearning. This paper give solution by developing e-Learning system based on community motivation model which able to overcome the problems regarding to user motivation in the implementation of e-Learning system. Model is developed based on the theory of learning motivation and requirement capturing from the requirement engineering?s field. The indicators used to measure the model effectiveness are hit and visit statistics, traffic ranking, comparison with other similar e-Learning system, and the relation between concepts in model. Community motivation model have been implemented in web based public e-Learning systems (IlmuKomputer.Com), with the significant results appeare

Combining Particle Swarm Optimization Based Feature Selection And Bagging Technique For Software Defect Prediction

The costs of finding and correcting software defects have been the most expensive activity in software development. The accurate prediction of defect‐prone software modules can help the software testing effort,reduce costs,and improve the software testing process by focusing on fault-prone module.Recently,static code attributes are used as defect predictors in software defect prediction research,since they are useful,generalizable,easy to use, and widely used.However,two common aspects of data quality that can affect performance of software defect prediction are class imbalance and noisy attributes.In this research,we propose the combination of particle swarm optimization and bagging technique for improving the accuracy of the software defect prediction.Particle swarm optimization is applied to deal with the feature selection,and bagging technique is employed to deal with the class imbalance problem.The proposed method is evaluated using the data sets from NASA metric data repository.Results have indicated that the proposed method makes an impressive improvement in prediction performance for most classifiers

Algoritma machine learning

Buku ini membahas berbagai algoritma machine learning yang merupakan kunci utama untuk memahami machine learning dari sisi pemrograman. Beberapa algoritma machine learning yang dikupas antara lain: Linear Regression, Logistic Regression, k-NN, K Means, Hierarchical Clustering, Naive Bayes, Random Forest, CART decision tree, Reinforecement Learning, Q-learning, Deep Learning, Support Vector Machine, dan Neural Network. Pada buku ini disertakan konsep dasar pengenalan bahasa Python untuk machine learning.Pada buku ini juga disertakan berbagai contoh latihan dan soal tes yang dapat dijadikan semacam tolak ukur untuk mengetahui seberapa besar pemahaman seseorang terhadap machine learning.xvi, 728 hlm.: 24 c

Extensible Requirements Patterns of Web Application for Efficient Web Application Development

Nowadays, web application has been going to be an e-business application. In the e-business environment, reducing time to market is a critical issue. Therefore, web application development process and time to market must be highly accelerated. On the other hand, a lot of redundant works actually occurred in the web application development. This paper presents the solutions for these problems, by proposing an extensible requirements pattern for efficient web application development. By reusing and reconstructing the extensible requirements patterns of web application, we can accelerate time to market and solve redundancy problems in web application development

Dapat apa sih dari university? : pelajaran entrepreneur untuk (maha)siswa lugu

Buku ini bermanfaat untuk teman-teman dan adik-adik mahasiswa semua dalam menempuh kehidupan di dunia, khususnya yang bergerak di bidang teknologi informasi. Saya berharap bahwa perjuangan dan kerja keras kita semua akan membawa republik ke arah kebaikan.vi, 74 hlm.; 18 cm