{"title":"A Resourceful Approach in Security Testing to Protect Electronic Payment System Against Unforeseen Attack","authors":"Rajat Kumar Behera, A. Sahoo, A. Jena","doi":"10.4018/IJOSSP.2017070102","DOIUrl":"https://doi.org/10.4018/IJOSSP.2017070102","url":null,"abstract":"This article describes how electronic payments are financial transactions made over the internet for goods or services. In the digital era, the e-commerce industry has gone beyond the traditional in-store service due to the wide spread of internet-based shopping. Developed countries are greatly relying on e-commerce business and a sizable number of countries have shown concern in regard to the online payment cards such as credit cards, debit cards, e-cash, e-cheques, e-wallets and smart card security. The main downsides are concerns over privacy or a malicious attack and hence safeguard mechanisms are required to protect personal information from falling into the hands of intruders. Before commercializing electronic payment systems (EPS), security tests play a significant role in the software development life cycle to check whether the system is secure and it is safe to use. A resourceful approach covering security policies, secure coding, security attack prevention methodology, security testing tool, security testing metrics, security test case prioritization techniques and a model for effective project management methodology are presented in this article. Early detection and resolution of security weaknesses can be achieved with the authors' proposed approach and would certainly reduce the time, effort and cost of a project. The proposed approach is likely the best-fit implementation of the payment industry, covering channels like B2C (Business to Consumer), C2C (Consumer to Consumer), C2B (Consumer to Business), B2B (Business to Business), People to People (P2P), G2C (Government to Citizen) and C2G (Citizen to Government).","PeriodicalId":53605,"journal":{"name":"International Journal of Open Source Software and Processes","volume":"88 1","pages":"24-48"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79166305","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}
{"title":"A Novel UML Based Approach for Early Detection of Change Prone Classes","authors":"Deepa Bura, A. Choudhary, R. K. Singh","doi":"10.4018/IJOSSP.2017070101","DOIUrl":"https://doi.org/10.4018/IJOSSP.2017070101","url":null,"abstract":"This article describes how predicting change-prone classes is essential for effective development of software. Evaluating changes from one release of software to the next can enhance software quality. This article proposes an efficient novel-based approach for predicting changes early in the object-oriented software. Earlier researchers have calculated change prone classes using static characteristics such as source line of code e.g. added, deleted and modified. This research work proposes to use dynamic metrics such as execution duration, run time information, regularity, class dependency and popularity for predicting change prone classes. Execution duration and run time information are evaluated directly from the software. Class dependency is obtained from UML2.0 class and sequence diagrams. Regularity and popularity is acquired from frequent item set mining algorithms and an ABC algorithm. For classifying the class as change-prone or non-change-prone class an Interactive Dichotomizer version 3 ID3 algorithm is used. Further validation of the results is done using two open source software, OpenClinic and OpenHospital.","PeriodicalId":53605,"journal":{"name":"International Journal of Open Source Software and Processes","volume":"22 1","pages":"1-23"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79070980","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}
{"title":"A Novel Anti-Obfuscation Model for Detecting Malicious Code","authors":"Yuehan Wang, Tong Li, Yongquan Cai, Zhenhu Ning, Fei Xue, Di Jiao","doi":"10.4018/IJOSSP.2017040102","DOIUrl":"https://doi.org/10.4018/IJOSSP.2017040102","url":null,"abstract":"In this article, the authors present a new malicious code detection model. The detection model improves typical n-gram feature extraction algorithms that are easy to be obfuscated. Specifically, the proposed model can dynamically determine obfuscation features and then adjust the selection of meaningful features to improve corresponding machine learning analysis. The experimental results show that the feature database, which is built based on the proposed feature selection and cleaning method, contains a stable number of features and can automatically get rid of obfuscation features. Overall, the proposed detection model has features of long timeliness, high applicability and high accuracy of identification.","PeriodicalId":53605,"journal":{"name":"International Journal of Open Source Software and Processes","volume":"46 1","pages":"25-43"},"PeriodicalIF":0.0,"publicationDate":"2017-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84651279","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}
Feras Hanandeh, A. Saifan, Mohammed Akour, Noor Khamis Al-Hussein, K. Shatnawi
{"title":"Evaluating Maintainability of Open Source Software: A Case Study","authors":"Feras Hanandeh, A. Saifan, Mohammed Akour, Noor Khamis Al-Hussein, K. Shatnawi","doi":"10.4018/IJOSSP.2017010101","DOIUrl":"https://doi.org/10.4018/IJOSSP.2017010101","url":null,"abstract":"Maintainability is one of the most important quality attribute that affect the quality of software. There are four factors that affect the maintainability of software which are: analyzability, changeability, stability, and testability. Open source software OSS developed by collaborative work done by volunteers through around the world with different management styles. Open source code is updated and modified all the time from the first release. Therefore, there is a need to measure the quality and specifically the maintainability of such code. This paper discusses the maintainability for the three domains of the open source software. The domains are: education, business and game. Moreover, to observe the most effective metrics that directly affects the maintainability of software. Analysis of the results demonstrates that OSS in the education domain is the most maintainable code and cl_stat number of executable statements metric has the highest degree of influence on the calculation of maintenance in all three domains.","PeriodicalId":53605,"journal":{"name":"International Journal of Open Source Software and Processes","volume":"37 1","pages":"1-20"},"PeriodicalIF":0.0,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86853428","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}
{"title":"Investigating the Effect of Sensitivity and Severity Analysis on Fault Proneness in Open Source Software","authors":"D. Mala","doi":"10.4018/IJOSSP.2017010103","DOIUrl":"https://doi.org/10.4018/IJOSSP.2017010103","url":null,"abstract":"Fault prone components in open source software leads to huge loss and inadvertent effects if not properly identified and rigorously tested. Most of the reported studies in the literature have applied design metrics alone, to identify such critical components. But in reality, some of the components' criticality level can be identified only by means of dynamic code analysis; as some of the components seem to be normal but still have higher level of impact on the other components. This leads to an insight on the need of a rigorous analysis based on how sensitive a component is and how severe will be the impact of it on other components in the system. To achieve this, an efficient mechanism of evaluating the criticality index of each component by means of sensitivity and severity analysis using the static design metrics and dynamic source code metrics has been proposed. Then, testing is conducted rigorously on these components using both unit testing and pair-wise integration testing.","PeriodicalId":53605,"journal":{"name":"International Journal of Open Source Software and Processes","volume":"9 1","pages":"42-66"},"PeriodicalIF":0.0,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82243177","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}
{"title":"A New Data Mining-Based Framework to Test Case Prioritization Using Software Defect Prediction","authors":"E. Alsukhni, A. Saifan, Hanadi Alawneh","doi":"10.4018/IJOSSP.2017010102","DOIUrl":"https://doi.org/10.4018/IJOSSP.2017010102","url":null,"abstract":"Testcasesdonothavethesameimportancewhenusedtodetectfaultsinsoftware;therefore,itis moreefficienttotestthesystemwiththetestcasesthathavetheabilitytodetectthefaults.This researchproposesanewframeworkthatcombinesdataminingtechniquestoprioritizethetestcases. Itenhancesfaultpredictionanddetectionusingtwodifferenttechniques:1)thedataminingregression classifierthatdependsonsoftwaremetricstopredictdefectivemodules,and2)thek-meansclustering techniquethatisusedtoselectandprioritizetestcasestoidentifythefaultearly.Ourapproachof testcaseprioritizationyieldsgoodresultsincomparisonwithotherstudies.Theauthorsusedthe AveragePercentageofFaultsDetection(APFD)metrictoevaluatetheproposedframework,which resultsin19.9%forallsystemmodulesand25.7%fordefectiveones.Ourresultsgiveusanindication thatitiseffectivetostartthetestingprocesswiththemostdefectivemodulesinsteadoftestingall modulesarbitraryarbitrarily. KeywORDS Data Mining, Software Defect Prediction, Software Testing, Test Case Prioritization","PeriodicalId":53605,"journal":{"name":"International Journal of Open Source Software and Processes","volume":"15 1","pages":"21-41"},"PeriodicalIF":0.0,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84765108","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}