Traditional software development is shifting toward the open-source development model, particularly in the current environment of competitive challenges to develop software openly. The author employs a case study approach to investigate how organizations and their affiliated developers collaborate in the open-source software (OSS) ecosystem TensorFlow (TF). The analysis of the artificial intelligence OSS library TF combines social network analysis (SNA) and an examination of archival data by mining software repositories. The study looks at the structure and evolution of code-collaboration among developers and with the ecosystem's organizational networks over the TF lifespan. These involved organizations play a particularly critical role in development. The research also looks at productivity, homophily, development, and diversity among developers. The results deepen the understanding of OSS communities' collaborative developer and organization patterns. Furthermore, the study emphasizes the importance and evolution of social networks, diversity, and productivity in ecosystems.
{"title":"Organizational Influencers in Open-Source Software Projects","authors":"R. Schreiber","doi":"10.4018/ijossp.318400","DOIUrl":"https://doi.org/10.4018/ijossp.318400","url":null,"abstract":"Traditional software development is shifting toward the open-source development model, particularly in the current environment of competitive challenges to develop software openly. The author employs a case study approach to investigate how organizations and their affiliated developers collaborate in the open-source software (OSS) ecosystem TensorFlow (TF). The analysis of the artificial intelligence OSS library TF combines social network analysis (SNA) and an examination of archival data by mining software repositories. The study looks at the structure and evolution of code-collaboration among developers and with the ecosystem's organizational networks over the TF lifespan. These involved organizations play a particularly critical role in development. The research also looks at productivity, homophily, development, and diversity among developers. The results deepen the understanding of OSS communities' collaborative developer and organization patterns. Furthermore, the study emphasizes the importance and evolution of social networks, diversity, and productivity in ecosystems.","PeriodicalId":53605,"journal":{"name":"International Journal of Open Source Software and Processes","volume":"19 1","pages":"1-20"},"PeriodicalIF":0.0,"publicationDate":"2023-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79294065","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}
Classification of software defects is an important task to know the type of defects. It helps to prioritize the defects, to understand the cause of defects for improving the process of software defect management system by taking the appropriate action. In this paper, we evaluate the performance of naïve Bayes, support vector machine, k nearest neighbor, random forest, and decision tree machine learning algorithm to classify the software defect based on orthogonal defect classification by selecting the relevant features using chi-square score. Standard metrics accuracy, precision, and recall are calculated separately for Cassandra, HBase, and MongoDB datasets. The proposed method improves the existing approach in terms of accuracy by 5%, 20%, 6%, 27%, and 26% for activity, defect impact, target, type, and qualifier respectively, and shows the enhanced performance.
{"title":"Classification of Software Defects Using Orthogonal Defect Classification","authors":"Sushil Kumar, S. K. Muttoo, V. Singh","doi":"10.4018/ijossp.300749","DOIUrl":"https://doi.org/10.4018/ijossp.300749","url":null,"abstract":"Classification of software defects is an important task to know the type of defects. It helps to prioritize the defects, to understand the cause of defects for improving the process of software defect management system by taking the appropriate action. In this paper, we evaluate the performance of naïve Bayes, support vector machine, k nearest neighbor, random forest, and decision tree machine learning algorithm to classify the software defect based on orthogonal defect classification by selecting the relevant features using chi-square score. Standard metrics accuracy, precision, and recall are calculated separately for Cassandra, HBase, and MongoDB datasets. The proposed method improves the existing approach in terms of accuracy by 5%, 20%, 6%, 27%, and 26% for activity, defect impact, target, type, and qualifier respectively, and shows the enhanced performance.","PeriodicalId":53605,"journal":{"name":"International Journal of Open Source Software and Processes","volume":"5 1","pages":"1-16"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89898581","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}
The wide usage of open source software (OSS) results in an increase of bug data forming an integral part of the extensive data ecosystem. This bug report data needs to be analyzed for bug fixing and prediction of various important attributes like bug severity, priority, fix time, assignees, etc. The increased volume of bug data and different bug reporters from different geographical locations make veracity an important concern. We assume that the bug reports (i.e., different bug attributes) reported in software bug repositories are trustworthy during the bug triaging process. In reality, the bug report data are not trustworthy regarding various aspects like integrity, authenticity, and trusted origin as the bugs are reported by users who may or may not have proper knowledge of the software. In this paper, we proposed entropy-based models for veracity estimation of different bug attributes.
{"title":"Evaluating the Veracity of Software Bug Reports using Entropy-based Measures","authors":"Madhu Kumari, V. B. Singh, Meera Sharma","doi":"10.4018/ijossp.315280","DOIUrl":"https://doi.org/10.4018/ijossp.315280","url":null,"abstract":"The wide usage of open source software (OSS) results in an increase of bug data forming an integral part of the extensive data ecosystem. This bug report data needs to be analyzed for bug fixing and prediction of various important attributes like bug severity, priority, fix time, assignees, etc. The increased volume of bug data and different bug reporters from different geographical locations make veracity an important concern. We assume that the bug reports (i.e., different bug attributes) reported in software bug repositories are trustworthy during the bug triaging process. In reality, the bug report data are not trustworthy regarding various aspects like integrity, authenticity, and trusted origin as the bugs are reported by users who may or may not have proper knowledge of the software. In this paper, we proposed entropy-based models for veracity estimation of different bug attributes.","PeriodicalId":53605,"journal":{"name":"International Journal of Open Source Software and Processes","volume":"32 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81921905","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}
Mobile applications (i.e., mobile apps) are dynamic from the user point of view, it is complex in some situations, and it is being developed in competitive and strict time frames. Therefore, the developers are required to pay attention to choose an appropriate software development process to cater these needs. Over the last decade, agile approaches have become very popular for software development in general, but there has only been limited research performed in the applicability of agile methods in the area of mobile app development. In this paper, a detailed review on mobile app development approaches with their best practices is prepared to explore the suitability of agile approaches. We have also conducted an online survey to know the current mobile app development trends in industries. It is observed that the agile approaches are the most suited approaches for mobile app development due to its flexibility in making changes, scalability, usability, etc. This crucial survey contributes towards a better comprehension of development trends in mobile app development industries.
{"title":"A Survey on Mobile App Development Approaches with Industry Perspective","authors":"","doi":"10.4018/ijossp.300754","DOIUrl":"https://doi.org/10.4018/ijossp.300754","url":null,"abstract":"Mobile applications (i.e., mobile apps) are dynamic from the user point of view, it is complex in some situations, and it is being developed in competitive and strict time frames. Therefore, the developers are required to pay attention to choose an appropriate software development process to cater these needs. Over the last decade, agile approaches have become very popular for software development in general, but there has only been limited research performed in the applicability of agile methods in the area of mobile app development. In this paper, a detailed review on mobile app development approaches with their best practices is prepared to explore the suitability of agile approaches. We have also conducted an online survey to know the current mobile app development trends in industries. It is observed that the agile approaches are the most suited approaches for mobile app development due to its flexibility in making changes, scalability, usability, etc. This crucial survey contributes towards a better comprehension of development trends in mobile app development industries.","PeriodicalId":53605,"journal":{"name":"International Journal of Open Source Software and Processes","volume":"72 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82559823","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}
The emergency department (ED) faces great challenges during the corona pandemic, which has greatly affected the course of work, as the medical staff works to maintain the quality of service and more guarantee the health of patients and staff. With the rapid spread of the coronavirus, ED has become the focus of attention of many researchers, where most of them focused on reducing waiting time and the duration of the patient's length of stay (LOS) through the restructuring of ED. In this study, the authors propose a simulation model using the hierarchical coloured-petri nets (CP-Nets). Several improvement scenarios have been suggested in order to arrive at the optimal solution. Scenarios were compared and solutions were presented to decision makers. This study was conducted at the Hassani Abdelkader Hospital in Sidi Bel Abbes. The authors obtained a model that could be generalized to other hospitals.
{"title":"Modelling and Simulation of Patient Flow in the Emergency Department During the COVID-19 Pandemic Using Hierarchical Coloured Petri Net","authors":"Zouaoui Louhab, Fatma Boufera","doi":"10.4018/ijossp.308790","DOIUrl":"https://doi.org/10.4018/ijossp.308790","url":null,"abstract":"The emergency department (ED) faces great challenges during the corona pandemic, which has greatly affected the course of work, as the medical staff works to maintain the quality of service and more guarantee the health of patients and staff. With the rapid spread of the coronavirus, ED has become the focus of attention of many researchers, where most of them focused on reducing waiting time and the duration of the patient's length of stay (LOS) through the restructuring of ED. In this study, the authors propose a simulation model using the hierarchical coloured-petri nets (CP-Nets). Several improvement scenarios have been suggested in order to arrive at the optimal solution. Scenarios were compared and solutions were presented to decision makers. This study was conducted at the Hassani Abdelkader Hospital in Sidi Bel Abbes. The authors obtained a model that could be generalized to other hospitals.","PeriodicalId":53605,"journal":{"name":"International Journal of Open Source Software and Processes","volume":"39 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81138879","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}
Internet of things can be defined as collection of different physical that can converse with each other without any interruption. The IoT applications have the capability to change the current scenario of various domains such as health service, agriculture, and so on by offering services that can offer luxury to human life and also increases effectiveness. IoT follows open framework. Because of that it presents an opportunity to adversary for easily targeting system by performing various cyber-attacks. Existing well known cryptography solutions cannot be implemented in IoT as it has limitations in terms of resource ownership and also storage capacity. The authors have designed an efficient and secure multi-factor IoT authentication algorithm that is lightweight and provides the protection against different security threats such as MITM, replay, and location spoofing threat. Suggested work is tested on the tool AVISPA for the security validation. Communication and computational cost is low for the suggested work in comparison with the existing work.
{"title":"Dynamic Key-Based and Context-Aware Internet of Things Authentication Approach","authors":"M. Mehta, Kajal S. Patel","doi":"10.4018/ijossp.310939","DOIUrl":"https://doi.org/10.4018/ijossp.310939","url":null,"abstract":"Internet of things can be defined as collection of different physical that can converse with each other without any interruption. The IoT applications have the capability to change the current scenario of various domains such as health service, agriculture, and so on by offering services that can offer luxury to human life and also increases effectiveness. IoT follows open framework. Because of that it presents an opportunity to adversary for easily targeting system by performing various cyber-attacks. Existing well known cryptography solutions cannot be implemented in IoT as it has limitations in terms of resource ownership and also storage capacity. The authors have designed an efficient and secure multi-factor IoT authentication algorithm that is lightweight and provides the protection against different security threats such as MITM, replay, and location spoofing threat. Suggested work is tested on the tool AVISPA for the security validation. Communication and computational cost is low for the suggested work in comparison with the existing work.","PeriodicalId":53605,"journal":{"name":"International Journal of Open Source Software and Processes","volume":"10 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89437891","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}
Cross-project refactoring prediction is prominent research that comprises model training from one project database and testing it for a database under a separate project. While performing the refactoring process on the cross project, software programs want to be restructured by modifying or adding the source code. However, recognizing a piece of code for predicting refactoring purposes is remained to be actual challenge for software designers. To date the entire refactoring procedure is highly dependent on the skills and software inventers. In this manuscript, a deep learning model is utilized to introduce a predictive model for refactoring to highlight classes that need to be refactored. Specifically, the deep learning technique is utilized along with the proposed attribute selection phases to predict refactoring at the class level. The planned optimized deep learning-based method for cross-project refactoring prediction is experimentally conducted on open- source project and accuracy found as 0.9648 as comparison to other mentioned state of the art.
{"title":"Cross Project Software Refactoring Prediction Using Optimized Deep Learning Neural Network with the Aid of Attribute Selection","authors":"","doi":"10.4018/ijossp.300756","DOIUrl":"https://doi.org/10.4018/ijossp.300756","url":null,"abstract":"Cross-project refactoring prediction is prominent research that comprises model training from one project database and testing it for a database under a separate project. While performing the refactoring process on the cross project, software programs want to be restructured by modifying or adding the source code. However, recognizing a piece of code for predicting refactoring purposes is remained to be actual challenge for software designers. To date the entire refactoring procedure is highly dependent on the skills and software inventers. In this manuscript, a deep learning model is utilized to introduce a predictive model for refactoring to highlight classes that need to be refactored. Specifically, the deep learning technique is utilized along with the proposed attribute selection phases to predict refactoring at the class level. The planned optimized deep learning-based method for cross-project refactoring prediction is experimentally conducted on open- source project and accuracy found as 0.9648 as comparison to other mentioned state of the art.","PeriodicalId":53605,"journal":{"name":"International Journal of Open Source Software and Processes","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78925719","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}
O. Tembhurne, Sonali Milmile, Ganesh R. Pathak, Atul O. Thakare, A. Thakare
A development of an orchestrator that manages multiusers and shared resources is extremely useful for automaton of jobs on the multiple machines. Many companies such as UiPath, Blue Prism, Automation Anywhere, etc. have their own orchestrator, but their orchestration services have some drawbacks (i.e., huge cost of yearly subscription, lack of user defined flexibility in architecture, and third-party security issues). The manuscript contains the design and analysis of orchestrator using open-source programming language and cloud platform (i.e., Python and OpenStack). The main focus of this paper is design and development of the orchestrator with shared resources and distribute environment management system that help to manage the multiuser, multi-machine environment in an efficient way. The overall examination delights that design and development of inhouse orchestrator application using open-source assets is not only useful in terms of colossal expense slicing, but also the development of free and flexible robotic process automation applications.
{"title":"An Orchestrator","authors":"O. Tembhurne, Sonali Milmile, Ganesh R. Pathak, Atul O. Thakare, A. Thakare","doi":"10.4018/ijossp.308792","DOIUrl":"https://doi.org/10.4018/ijossp.308792","url":null,"abstract":"A development of an orchestrator that manages multiusers and shared resources is extremely useful for automaton of jobs on the multiple machines. Many companies such as UiPath, Blue Prism, Automation Anywhere, etc. have their own orchestrator, but their orchestration services have some drawbacks (i.e., huge cost of yearly subscription, lack of user defined flexibility in architecture, and third-party security issues). The manuscript contains the design and analysis of orchestrator using open-source programming language and cloud platform (i.e., Python and OpenStack). The main focus of this paper is design and development of the orchestrator with shared resources and distribute environment management system that help to manage the multiuser, multi-machine environment in an efficient way. The overall examination delights that design and development of inhouse orchestrator application using open-source assets is not only useful in terms of colossal expense slicing, but also the development of free and flexible robotic process automation applications.","PeriodicalId":53605,"journal":{"name":"International Journal of Open Source Software and Processes","volume":"25 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82773191","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}
Bug triage is an essential task in the software maintenance phase. It is the process of assigning a developer (fixer) to a bug report. A personnel (triager) has to analyze the developers' profiles and bug reports for the purpose of making a suitable assignment. Manual bug triage consumes time and effort, so automating this process is a necessity. The previous research studies addressed the triage problem as an information retrieval problem, where the new bug report is the query. Other researchers tackled this problem as a classification problem and utilized traditional machine learning or deep learning techniques. A handful of research studies handled this problem as an optimization problem and utilized optimization algorithms such as Hungarian. This paper briefs and analyzes the previous bug triage approaches in addition to conducting an empirical comparison among five of the previous approaches.
{"title":"Bug Triage Automation Approaches","authors":"Madonna Fanoos, A. Hamdy, K. Nagaty","doi":"10.4018/ijossp.313183","DOIUrl":"https://doi.org/10.4018/ijossp.313183","url":null,"abstract":"Bug triage is an essential task in the software maintenance phase. It is the process of assigning a developer (fixer) to a bug report. A personnel (triager) has to analyze the developers' profiles and bug reports for the purpose of making a suitable assignment. Manual bug triage consumes time and effort, so automating this process is a necessity. The previous research studies addressed the triage problem as an information retrieval problem, where the new bug report is the query. Other researchers tackled this problem as a classification problem and utilized traditional machine learning or deep learning techniques. A handful of research studies handled this problem as an optimization problem and utilized optimization algorithms such as Hungarian. This paper briefs and analyzes the previous bug triage approaches in addition to conducting an empirical comparison among five of the previous approaches.","PeriodicalId":53605,"journal":{"name":"International Journal of Open Source Software and Processes","volume":"38 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79962139","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}
Open source software is usually released while it still contains bugs. In order to fix a reported bug during maintenance phase, the developer has to search the source code files to identify the faulty ones; this process is called bug localization (BL). Automating BL is a necessity to boost the developer's productivity and enhance the software quality. The paper proposes an information retrieval based approach for retrieving and ranking a list of suspicious faulty source files relevant to a submitted bug report (BR). The proposed approach leverages textual features of the BRs and source files, which are parts-of-speech tagging, lexical and semantic similarity between the source files and BRs, in addition to the source file change history. The effectiveness of the proposed approach was evaluated over three open-source software repositories. Experimental results showed the superiority of the proposed approach over eight previous approaches in terms of top@N and MAP metrics.
{"title":"Locating Faulty Source Code Files to Fix Bug Reports","authors":"A. Hamdy, Abdelrahman E. Arabi","doi":"10.4018/ijossp.308791","DOIUrl":"https://doi.org/10.4018/ijossp.308791","url":null,"abstract":"Open source software is usually released while it still contains bugs. In order to fix a reported bug during maintenance phase, the developer has to search the source code files to identify the faulty ones; this process is called bug localization (BL). Automating BL is a necessity to boost the developer's productivity and enhance the software quality. The paper proposes an information retrieval based approach for retrieving and ranking a list of suspicious faulty source files relevant to a submitted bug report (BR). The proposed approach leverages textual features of the BRs and source files, which are parts-of-speech tagging, lexical and semantic similarity between the source files and BRs, in addition to the source file change history. The effectiveness of the proposed approach was evaluated over three open-source software repositories. Experimental results showed the superiority of the proposed approach over eight previous approaches in terms of top@N and MAP metrics.","PeriodicalId":53605,"journal":{"name":"International Journal of Open Source Software and Processes","volume":"87 10 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87688956","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：