Analyze the Performance of Software by Machine Learning Methods for Fault Prediction Techniques

Trend of using the software in daily life is increasing day by day. Software system development is growing more difficult as these technologies are integrated into daily life. Therefore, creating highly effective software is a significant difficulty. The quality of any software system continues to be the most important element among all the required characteristics. Nearly one-third of the total cost of software development goes toward testing. Therefore, it is always advantageous to find a software bug early in the software development process because if it is not found early, it will drive up the cost of the software development. This type of issue is intended to be resolved via software fault prediction. There is always a need for a better and enhanced prediction model in order to forecast the fault before the real testing and so reduce the flaws in the time and expense of software projects. The various machine learning techniques for classifying software bugs are discussed in this paper

Perbandingan analisis sentimen PLN Mobile: Machine learning vs. deep learning

Play Store app ratings hold significant value as they offer critical insights for app developers to enhance digital service quality. The research centers on the PLN Mobile app, which has garnered mixed user opinions since its launch. These reviews come with challenges for users and developers when interpreting user comments. This study conducts tests,comparing several machine learning algorithms: logistic regression, decision trees, random forests, and specific deep learning algorithms, including neural network multi-layer perceptron (MLP) and long short-term memory (LSTM) for sentiment classification, i.e., positive or negative. The study collected 3,000 PLN Mobile user reviews, comprising 1,965 positive and 1,035 negative reviews. Logistic regression achieved an 84.47% accuracy rate, decision trees scored 79.30%, and random forests reached 83.64%. In contrast, deep learning models, particularly the Neural Network Multilayer Perceptron (MLP), reached an accuracy rate of 84.47%, while the LSTM achieved an accuracy rate of 78.83%. In the context of sentiment analysis of PLN Mobile user reviews, machine learning models using the logistic regression algorithm and deep learning models employing the multi-layer perceptron (MLP) neural network algorithm demonstrated higher accuracy compared to other methods

Security Bug Report Classification using Feature Selection, Clustering, and Deep Learning

As the numbers of software vulnerabilities and cybersecurity threats increase, it is becoming more difficult and time consuming to classify bug reports manually. This thesis is focused on exploring techniques that have potential to improve the performance of automated classification of software bug reports as security or non-security related. Using supervised learning, feature selection was used to engineer new feature vectors to be used in machine learning. Feature selection changes the vocabulary used by selecting words with the greatest impact on classification. Feature selection was able to increase the F-Score across the datasets by increasing the precision. We also explored unsupervised classification based on clustering. A distribution of software issues was created using variational autoencoders, where the majority of security related issues were closely related. However, a portion of non-security issues also ended up in the distribution. Furthermore, we explored recent advances in text mining classification based on deep learning. Specifically, we used recurrent networks for supervised and semi-supervised classification. LSTM networks outperformed the Naive Bayes classifier in projects with a high ratio of security related issues. Sequence autoencoders were trained on unlabeled data and tuned with labeled data. The results showed that using unlabeled software issues different from the testing datasets degraded the results. Sequence autoencoders may be used on large datasets, where labeled data is scarce