Production Systems and Information Engineering最新文献

Reverse geoencoding based route distance calculator 基于反向地理编码的路线距离计算器

Production Systems and Information Engineering Pub Date : 1900-01-01 DOI: 10.32968/psaie.2022.3.4

O. Hornyák

引用次数: 0

RelOntoUML model of the archaeological findings 考古发现的RelOntoUML模型

Production Systems and Information Engineering Pub Date : 1900-01-01 DOI: 10.32968/psaie.2022.1.5.

A. Agárdi

引用次数: 0

A new approach to software defect prediction based on convolutional neural network and bidirectional long short-term memory 基于卷积神经网络和双向长短期记忆的软件缺陷预测新方法

Production Systems and Information Engineering Pub Date : 1900-01-01 DOI: 10.32968/psaie.2022.3.1

N. A. A. Khleel, K. Nehéz

{"title":"A new approach to software defect prediction based on convolutional neural network and bidirectional long short-term memory","authors":"N. A. A. Khleel, K. Nehéz","doi":"10.32968/psaie.2022.3.1","DOIUrl":"https://doi.org/10.32968/psaie.2022.3.1","url":null,"abstract":"Software defect prediction (SDP) plays an important role in improving software quality and reliability while reducing software maintenance cost. The problem in the field of SDP is how to determine the defective source code with high accuracy. To build more accurate predictor models, a lot of features are presented, e.g., static code features, social network features, and process features, etc. Several machine learning (ML) and deep learning (DL) algorithms have been developed and adopted to identify and remove defects from the source code, where previous studies have proved that DL algorithms are promising techniques for predicting software defects. The aim of this study is to investigate the prediction performance of two DL algorithms namely, Convolutional Neural Network (CNN) and Bidirectional Long short-term memory (BI-LSTM) in the domain of SDP. To establish the effectiveness of the proposed approach, the experiments were conducted on the available benchmark datasets which obtained from open-source java projects GitHub repository and the models were evaluated by applying seven evaluation metrics which are accuracy, precision, recall, f-measure, matthews correlation coefficient (MCC), area under the ROC curve (AUC), mean square error (MSE). We found out that the best accuracy obtained on training dataset is 81% by using CNN model, while the best accuracy obtained on validation dataset is 80% by using BI-LSTM model. The best AUC obtained on training dataset is 88% by using CNN model, while the best AUC obtained on validation dataset is 83% by using the both models. It is nearly impossible to rule which model is better than the other so every model can be analyzed separately and the best model according to the problem at hand can be used, therefore, based on the problem of this study, The evaluation results show the effectiveness of our proposed models based on standard performance evaluation criteria.","PeriodicalId":117509,"journal":{"name":"Production Systems and Information Engineering","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128075984","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

An approach to classify algorithms by complexity 一种按复杂度对算法进行分类的方法

Production Systems and Information Engineering Pub Date : 1900-01-01 DOI: 10.32968/psaie.2022.3.8

O. Hornyák

引用次数: 0

Új módszer dolgozók munkájának közvetlen irányítására rugalmas gyártórendszerben

Production Systems and Information Engineering Pub Date : 1900-01-01 DOI: 10.32968/psaie.2022.4.3

Norbert Tóth, Gyula Kulcsár

引用次数: 0

Automated data-collection for personalized facial expression recognition in human-robot interaction 人机交互中个性化面部表情识别的自动数据采集

Production Systems and Information Engineering Pub Date : 1900-01-01 DOI: 10.32968/psaie.2022.2.6.

F. Adjailia, P. Sinčák

引用次数: 0

Analysis of the Maximal Pattern Mining method and its variants 最大模式挖掘方法及其变体分析

Production Systems and Information Engineering Pub Date : 1900-01-01 DOI: 10.32968/psaie.2022.2.7.

Dávid Gégény, S. Radeleczki

引用次数: 0

Conversion of companies and source code comment ontology into RelOntoUML 将公司和源代码注释本体转换为RelOntoUML

Production Systems and Information Engineering Pub Date : 1900-01-01 DOI: 10.32968/psaie.2022.1.9.

A. Agárdi

引用次数: 0

Protection against remote desktop attacks 远程桌面攻击防范

Production Systems and Information Engineering Pub Date : 1900-01-01 DOI: 10.32968/psaie.2022.3.3

O. Hornyák

引用次数: 0

Software for the generalization of the Vehicle Routing Problem 车辆路线问题的泛化软件

Production Systems and Information Engineering Pub Date : 1900-01-01 DOI: 10.32968/psaie.2022.3.5

A. Agárdi

引用次数: 1