Pub Date : 2021-07-01DOI: 10.4018/ijossp.2021070101
Mokhtaria Bouslama, M. Abdi
The cost of software maintenance is always increasing. The companies are often confronted to failures and software errors. The quality of software to use is so required. In this paper, the authors propose a new formal approach for assessing the quality of object-oriented system design according to the quality assessment model. This approach consists in modeling the input software system by an automaton based on object-oriented design metrics and their relationship with the quality attributes. The model exhibits the importance of metrics through their links with the attributes of software quality. In addition, it is very practical and flexible for all changes. It allows the quality estimation and its validation. For the verification of proposed probabilistic model (automaton), they use the model-checking and the prism tool. The model-checking is very interesting for the evaluation and validation of the probabilistic automaton. They use it to approve the software quality of the three experimental projects. The obtained results are very interesting and of great importance.
{"title":"Towards a Formal Approach for Assessing the Design Quality of Object-Oriented Systems","authors":"Mokhtaria Bouslama, M. Abdi","doi":"10.4018/ijossp.2021070101","DOIUrl":"https://doi.org/10.4018/ijossp.2021070101","url":null,"abstract":"The cost of software maintenance is always increasing. The companies are often confronted to failures and software errors. The quality of software to use is so required. In this paper, the authors propose a new formal approach for assessing the quality of object-oriented system design according to the quality assessment model. This approach consists in modeling the input software system by an automaton based on object-oriented design metrics and their relationship with the quality attributes. The model exhibits the importance of metrics through their links with the attributes of software quality. In addition, it is very practical and flexible for all changes. It allows the quality estimation and its validation. For the verification of proposed probabilistic model (automaton), they use the model-checking and the prism tool. The model-checking is very interesting for the evaluation and validation of the probabilistic automaton. They use it to approve the software quality of the three experimental projects. The obtained results are very interesting and of great importance.","PeriodicalId":53605,"journal":{"name":"International Journal of Open Source Software and Processes","volume":"41 1","pages":"1-16"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86075812","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}
Pub Date : 2021-07-01DOI: 10.4018/IJOSSP.2021070104
H. Alyami, Wael Alosaimi, M. Krichen, Roobaea Alroobaea
To restrict COVID-19, individuals must remain two meters away from one another in public since public health authorities find this a healthy distance. In this way, the incidence of “social distancing” keeps pace with COVID-19 spread. For this purpose, the proposed solution consists of the development of a tool based on AI technologies which takes as input videos (in real time) from streets and public spaces and gives as output the places where social distancing is not respected. Detected persons who are not respecting social distancing are surrounded with red rectangles and those who respect social distancing with green rectangles. The solution has been tested for the case of videos from the two Holy Mosques in Saudi Arabia: Makkah and Madinah. As a novel contribution compared to existent approaches in the literature, the solution allows the detection of the age, class, and sex of persons not respecting social distancing. Person detection is performed using the Faster RCNN with ResNet-50 as it is the backbone network that is pre-trained with the open source COCO dataset. The obtained results are satisfactory and may be improved by considering more sophisticated cameras, material, and techniques.
{"title":"Monitoring Social Distancing Using Artificial Intelligence for Fighting COVID-19 Virus Spread","authors":"H. Alyami, Wael Alosaimi, M. Krichen, Roobaea Alroobaea","doi":"10.4018/IJOSSP.2021070104","DOIUrl":"https://doi.org/10.4018/IJOSSP.2021070104","url":null,"abstract":"To restrict COVID-19, individuals must remain two meters away from one another in public since public health authorities find this a healthy distance. In this way, the incidence of “social distancing” keeps pace with COVID-19 spread. For this purpose, the proposed solution consists of the development of a tool based on AI technologies which takes as input videos (in real time) from streets and public spaces and gives as output the places where social distancing is not respected. Detected persons who are not respecting social distancing are surrounded with red rectangles and those who respect social distancing with green rectangles. The solution has been tested for the case of videos from the two Holy Mosques in Saudi Arabia: Makkah and Madinah. As a novel contribution compared to existent approaches in the literature, the solution allows the detection of the age, class, and sex of persons not respecting social distancing. Person detection is performed using the Faster RCNN with ResNet-50 as it is the backbone network that is pre-trained with the open source COCO dataset. The obtained results are satisfactory and may be improved by considering more sophisticated cameras, material, and techniques.","PeriodicalId":53605,"journal":{"name":"International Journal of Open Source Software and Processes","volume":"77 8 1","pages":"48-63"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84246759","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}
Pub Date : 2021-04-01DOI: 10.4018/IJOSSP.2021040101
Nagwa R. Fisal, A. Hamdy, E. Rashed
Regression testing is one of the essential activities during the maintenance phase of software projects. It is executed to ensure the validity of an altered software. However, as the software evolves, regression testing becomes prohibitively expensive. In order to reduce the cost of regression testing, it is mandatory to reduce the size of the test suite by selecting the most representative test cases that do not compromise the effectiveness of the regression testing in terms of fault-detection capability. This problem is known as test suite reduction (TSR) problem, and it is known to be an NP-complete. The paper proposes a multi-objective adapted binary bat algorithm (ABBA) to solve the TSR problem. The original binary bat (OBBA) algorithm was adapted to enhance its exploration capabilities during the search for a Pareto-optimal surface. The effectiveness of the ABBA was evaluated using six Java programs with different sizes. Experimental results showed that for the same fault discovery rate, the ABBA is capable of reducing the test suite size more than the OBBA and the BPSO.
{"title":"Search-Based Regression Testing Optimization","authors":"Nagwa R. Fisal, A. Hamdy, E. Rashed","doi":"10.4018/IJOSSP.2021040101","DOIUrl":"https://doi.org/10.4018/IJOSSP.2021040101","url":null,"abstract":"Regression testing is one of the essential activities during the maintenance phase of software projects. It is executed to ensure the validity of an altered software. However, as the software evolves, regression testing becomes prohibitively expensive. In order to reduce the cost of regression testing, it is mandatory to reduce the size of the test suite by selecting the most representative test cases that do not compromise the effectiveness of the regression testing in terms of fault-detection capability. This problem is known as test suite reduction (TSR) problem, and it is known to be an NP-complete. The paper proposes a multi-objective adapted binary bat algorithm (ABBA) to solve the TSR problem. The original binary bat (OBBA) algorithm was adapted to enhance its exploration capabilities during the search for a Pareto-optimal surface. The effectiveness of the ABBA was evaluated using six Java programs with different sizes. Experimental results showed that for the same fault discovery rate, the ABBA is capable of reducing the test suite size more than the OBBA and the BPSO.","PeriodicalId":53605,"journal":{"name":"International Journal of Open Source Software and Processes","volume":"4 1","pages":"1-20"},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84654521","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}
Pub Date : 2021-04-01DOI: 10.4018/IJOSSP.2021040102
Archana Patnaik, Neelamadhab Padhy
Code smell aims to identify bugs that occurred during software development. It is the task of identifying design problems. The significant causes of code smell are complexity in code, violation of programming rules, low modelling, and lack of unit-level testing by the developer. Different open source systems like JEdit, Eclipse, and ArgoUML are evaluated in this work. After collecting the data, the best features are selected using recursive feature elimination (RFE). In this paper, the authors have used different anomaly detection algorithms for efficient recognition of dirty code. The average accuracy value of k-means, GMM, autoencoder, PCA, and Bayesian networks is 98%, 94%, 96%, 89%, and 93%. The k-means clustering algorithm is the most suitable algorithm for code detection. Experimentally, the authors proved that ArgoUML project is having better performance as compared to Eclipse and JEdit projects.
{"title":"A Hybrid Approach to Identify Code Smell Using Machine Learning Algorithms","authors":"Archana Patnaik, Neelamadhab Padhy","doi":"10.4018/IJOSSP.2021040102","DOIUrl":"https://doi.org/10.4018/IJOSSP.2021040102","url":null,"abstract":"Code smell aims to identify bugs that occurred during software development. It is the task of identifying design problems. The significant causes of code smell are complexity in code, violation of programming rules, low modelling, and lack of unit-level testing by the developer. Different open source systems like JEdit, Eclipse, and ArgoUML are evaluated in this work. After collecting the data, the best features are selected using recursive feature elimination (RFE). In this paper, the authors have used different anomaly detection algorithms for efficient recognition of dirty code. The average accuracy value of k-means, GMM, autoencoder, PCA, and Bayesian networks is 98%, 94%, 96%, 89%, and 93%. The k-means clustering algorithm is the most suitable algorithm for code detection. Experimentally, the authors proved that ArgoUML project is having better performance as compared to Eclipse and JEdit projects.","PeriodicalId":53605,"journal":{"name":"International Journal of Open Source Software and Processes","volume":"39 1","pages":"21-35"},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88345727","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}
Pub Date : 2021-04-01DOI: 10.4018/IJOSSP.2021040103
Wasiur Rhmann
Software change prediction (SCP) is used for the prediction of changes earlier in the software development life cycle. It identifies the files that are change prone. Software maintenance costs can be reduced with the help of accurate prediction of change-prone files. Most of the literature of SCP deals with the identification of a class as change prone or not change prone. In the present work, the amount of change in a web project in terms of line of code added (loc_added), line of code deleted (loc_deleted), and lines of code (LOC) are predicted using time series forecasting method of machine learning. Data of web projects is obtained from GIT repository using Pydriller Python package extractor. The obtained result showed that support vector machine (SVM) is good for prediction of loc_added and loc_removed while the random forest is good for the prediction of LOC. Results advocate the use machine learning techniques for forecasting changes amount in web projects.
{"title":"Quantitative Software Change Prediction in Open Source Web Projects Using Time Series Forecasting","authors":"Wasiur Rhmann","doi":"10.4018/IJOSSP.2021040103","DOIUrl":"https://doi.org/10.4018/IJOSSP.2021040103","url":null,"abstract":"Software change prediction (SCP) is used for the prediction of changes earlier in the software development life cycle. It identifies the files that are change prone. Software maintenance costs can be reduced with the help of accurate prediction of change-prone files. Most of the literature of SCP deals with the identification of a class as change prone or not change prone. In the present work, the amount of change in a web project in terms of line of code added (loc_added), line of code deleted (loc_deleted), and lines of code (LOC) are predicted using time series forecasting method of machine learning. Data of web projects is obtained from GIT repository using Pydriller Python package extractor. The obtained result showed that support vector machine (SVM) is good for prediction of loc_added and loc_removed while the random forest is good for the prediction of LOC. Results advocate the use machine learning techniques for forecasting changes amount in web projects.","PeriodicalId":53605,"journal":{"name":"International Journal of Open Source Software and Processes","volume":"11 1","pages":"36-51"},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72815121","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}
Pub Date : 2021-03-03DOI: 10.1101/2021.03.02.21252747
C. Pandey
A Natural Language Processing (NLP) method was used to uncover various issues and sentiments surrounding COVID-19 from social media and get a deeper understanding of fluctuating public opinion in situations of wide-scale panic to guide improved decision making with the help of a sentiment analyser created for the automated extraction of COVID-19 related discussions based on topic modelling. Moreover, the BERT model was used for the sentiment classification of COVID-19 Reddit comments. These findings shed light on the importance of studying trends and using computational techniques to assess human psyche in times of distress.
{"title":"redBERT: A Topic Discovery and Deep SentimentClassification Model on COVID-19 OnlineDiscussions Using BERT NLP Model","authors":"C. Pandey","doi":"10.1101/2021.03.02.21252747","DOIUrl":"https://doi.org/10.1101/2021.03.02.21252747","url":null,"abstract":"A Natural Language Processing (NLP) method was used to uncover various issues and sentiments surrounding COVID-19 from social media and get a deeper understanding of fluctuating public opinion in situations of wide-scale panic to guide improved decision making with the help of a sentiment analyser created for the automated extraction of COVID-19 related discussions based on topic modelling. Moreover, the BERT model was used for the sentiment classification of COVID-19 Reddit comments. These findings shed light on the importance of studying trends and using computational techniques to assess human psyche in times of distress.","PeriodicalId":53605,"journal":{"name":"International Journal of Open Source Software and Processes","volume":"26 1","pages":"32-47"},"PeriodicalIF":0.0,"publicationDate":"2021-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88695084","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}
Pub Date : 2021-01-01DOI: 10.4018/IJOSSP.2021010103
Satyajeet Panigrahi, A. Jena
This paper introduces the technique to select the test cases from the unified modeling language (UML) behavioral diagram. The UML behavioral diagram describes the boundary, structure, and behavior of the system that is fed as input for generating the graph. The graph is constructed by assigning the weights, nodes, and edges. Then, test case sequences are created from the graph with minimal fitness value. Then, the optimal sequences are selected from the proposed fractional-spider monkey optimization (fractional-SMO). The developed fractional-SMO is designed by integrating fractional calculus and SMO. Thus, the efficient test cases are selected based on the optimization algorithm that uses fitness parameters, like coverage and fault. Simulations are performed via five synthetic UML diagrams taken from the dataset. The performance of the proposed technique is computed using coverage and the number of test cases. The maximal coverage of 49 and the minimal number of test cases as 2,562 indicate the superiority of the proposed technique.
{"title":"Optimization of Test Cases in Object-Oriented Systems Using Fractional-SMO","authors":"Satyajeet Panigrahi, A. Jena","doi":"10.4018/IJOSSP.2021010103","DOIUrl":"https://doi.org/10.4018/IJOSSP.2021010103","url":null,"abstract":"This paper introduces the technique to select the test cases from the unified modeling language (UML) behavioral diagram. The UML behavioral diagram describes the boundary, structure, and behavior of the system that is fed as input for generating the graph. The graph is constructed by assigning the weights, nodes, and edges. Then, test case sequences are created from the graph with minimal fitness value. Then, the optimal sequences are selected from the proposed fractional-spider monkey optimization (fractional-SMO). The developed fractional-SMO is designed by integrating fractional calculus and SMO. Thus, the efficient test cases are selected based on the optimization algorithm that uses fitness parameters, like coverage and fault. Simulations are performed via five synthetic UML diagrams taken from the dataset. The performance of the proposed technique is computed using coverage and the number of test cases. The maximal coverage of 49 and the minimal number of test cases as 2,562 indicate the superiority of the proposed technique.","PeriodicalId":53605,"journal":{"name":"International Journal of Open Source Software and Processes","volume":"282 3 1","pages":"41-59"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86565980","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}
Pub Date : 2021-01-01DOI: 10.4018/IJOSSP.2021010101
G. Niu, R. Segall, Zichen Zhao, Zhijian Wu
This paper discusses the definitions of open source software, free software and freeware, and the concept of big data. The authors then introduce R and Python as the two most popular open source statistical software (OSSS). Additional OSSS, such as JASP, PSPP, GRETL, SOFA Statistics, Octave, KNIME, and Scilab, are also introduced in this paper with function descriptions and modeling examples. They further discuss OSSS's capability in artificial intelligence application and modeling and Popular OSSS-based machine learning libraries and systems. The paper intends to provide a reference for readers to make proper selections of open source software when statistical analysis tasks are needed. In addition, working platform and selective numerical, descriptive and analysis examples are provided for each software. Readers could have a direct and in-depth understanding of each software and its functional highlights.
{"title":"A Survey of Open Source Statistical Software (OSSS) and Their Data Processing Functionalities","authors":"G. Niu, R. Segall, Zichen Zhao, Zhijian Wu","doi":"10.4018/IJOSSP.2021010101","DOIUrl":"https://doi.org/10.4018/IJOSSP.2021010101","url":null,"abstract":"This paper discusses the definitions of open source software, free software and freeware, and the concept of big data. The authors then introduce R and Python as the two most popular open source statistical software (OSSS). Additional OSSS, such as JASP, PSPP, GRETL, SOFA Statistics, Octave, KNIME, and Scilab, are also introduced in this paper with function descriptions and modeling examples. They further discuss OSSS's capability in artificial intelligence application and modeling and Popular OSSS-based machine learning libraries and systems. The paper intends to provide a reference for readers to make proper selections of open source software when statistical analysis tasks are needed. In addition, working platform and selective numerical, descriptive and analysis examples are provided for each software. Readers could have a direct and in-depth understanding of each software and its functional highlights.","PeriodicalId":53605,"journal":{"name":"International Journal of Open Source Software and Processes","volume":"47 1","pages":"1-20"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77594800","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}
Pub Date : 2021-01-01DOI: 10.4018/IJOSSP.2021010102
Kapil Kumar, Shyla, Vishal Bhatnagar
The movement towards digital era introduces centralization of information, web services, applications, and devices. The fraudster keeps an eye over ongoing transaction and forges data by using different techniques as traffic monitoring, session hijacking, phishing, and network bottleneck. In this study, the authors design a framework using deep learning algorithm to suspect the fraudulence transaction and evaluate the performance of the proposed system by updating data repositories. The neural network-based sequence classification technique is used for fraud detection of credit card transactions by including threshold value to measure the deviation of transaction. The reconstruction error (MSE) and predefined threshold value of 4.9 is used for determination of fraudulent transactions.
{"title":"Credit Card Fraud Transaction Detection System Using Neural Network-Based Sequence Classification Technique","authors":"Kapil Kumar, Shyla, Vishal Bhatnagar","doi":"10.4018/IJOSSP.2021010102","DOIUrl":"https://doi.org/10.4018/IJOSSP.2021010102","url":null,"abstract":"The movement towards digital era introduces centralization of information, web services, applications, and devices. The fraudster keeps an eye over ongoing transaction and forges data by using different techniques as traffic monitoring, session hijacking, phishing, and network bottleneck. In this study, the authors design a framework using deep learning algorithm to suspect the fraudulence transaction and evaluate the performance of the proposed system by updating data repositories. The neural network-based sequence classification technique is used for fraud detection of credit card transactions by including threshold value to measure the deviation of transaction. The reconstruction error (MSE) and predefined threshold value of 4.9 is used for determination of fraudulent transactions.","PeriodicalId":53605,"journal":{"name":"International Journal of Open Source Software and Processes","volume":"38 1","pages":"21-40"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88131081","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}
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