Automating Software Development using Artificial Intelligence

TL;DR: This study reviews AI techniques for Automated Code Generation (ACG) in software development, comparing traditional and AI methods, their challenges, and limitations, using evaluation metrics such as accuracy, efficiency, and scalability to identify strengths, weaknesses, and future applications.

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TL;DR: It is shown that the support vector machine (SVM) classification algorithm, a recent development from the machine learning community, proves its potential for structure-activity relationship analysis in a benchmark test, compared to several machine learning techniques currently used in the field.

TL;DR: In this paper, a support vector machine (SVM) was used to estimate the state of charge (SOC) of a high capacity LiFeMnPO4 battery cell from an experimental dataset using a SVM approach.

TL;DR: This paper views fault-proneness prediction as a binary classification task and uses Support Vector Machines (SVM) as a state-of-the-art classification method and performs a comparative experimental study of the effectiveness of ANNs and SVMs on a data set obtained from NASA's Metrics Data Program data repository.

TL;DR: The goal was to measure the accuracy of Random Forest in classifying Android application behavior to classify applications as malicious or benign, and to focus on detection accuracy as the free parameters of the Random Forest algorithm such as the number of trees, depth of each tree and number of random features selected are varied.