Ahmad Ahmad Muhammad, Abdelrahman Soliman, Hala Zayed, Ahmed H. Yousef, Sahar Selim
Abstract Context The most popular mobile platforms, Android and iOS, are traditionally developed using native programming languages—Java and Kotlin for Android, and Objective‐C followed by Swift for iOS, respectively. Due to their popularity, there is always a demand to convert applications written for one of these two platforms to another. Cross‐platform mobile development is widely used as a solution where an application is written once and deployed on multiple platforms written in several other programming languages. One common cross‐platform approach that has been used recently by some research groups is the Trans‐Compilation approach. They focus on translating a program written in iOS into Android or vice versa. The main problem with their solutions is that library function mapping is not generalized and usually functions constitute most of the parts of any program. Objective This study aims to introduce an automatic library mapping approach for mobile programming languages. Method A library function of a source language will be automatically mapped to a corresponding function of the destination language by using the function structure for the two languages. The function structure includes the library to which the function belongs, the return type, parameter types, and the number of parameters. To test our approach, we map from Swift to Java. Results The results of our experiments show that our automatic library mapping approach achieves an average accuracy of 83.6% when tested on the most used libraries and outperforms current state‐of‐the‐art mapping techniques in terms of mapping accuracy. Conclusion These findings show that our automatic mapping approach is promising and can help to overcome the limitations of the trans‐compilation approaches.
{"title":"Automated library mapping approach based on cross‐platform for mobile development programming languages","authors":"Ahmad Ahmad Muhammad, Abdelrahman Soliman, Hala Zayed, Ahmed H. Yousef, Sahar Selim","doi":"10.1002/spe.3281","DOIUrl":"https://doi.org/10.1002/spe.3281","url":null,"abstract":"Abstract Context The most popular mobile platforms, Android and iOS, are traditionally developed using native programming languages—Java and Kotlin for Android, and Objective‐C followed by Swift for iOS, respectively. Due to their popularity, there is always a demand to convert applications written for one of these two platforms to another. Cross‐platform mobile development is widely used as a solution where an application is written once and deployed on multiple platforms written in several other programming languages. One common cross‐platform approach that has been used recently by some research groups is the Trans‐Compilation approach. They focus on translating a program written in iOS into Android or vice versa. The main problem with their solutions is that library function mapping is not generalized and usually functions constitute most of the parts of any program. Objective This study aims to introduce an automatic library mapping approach for mobile programming languages. Method A library function of a source language will be automatically mapped to a corresponding function of the destination language by using the function structure for the two languages. The function structure includes the library to which the function belongs, the return type, parameter types, and the number of parameters. To test our approach, we map from Swift to Java. Results The results of our experiments show that our automatic library mapping approach achieves an average accuracy of 83.6% when tested on the most used libraries and outperforms current state‐of‐the‐art mapping techniques in terms of mapping accuracy. Conclusion These findings show that our automatic mapping approach is promising and can help to overcome the limitations of the trans‐compilation approaches.","PeriodicalId":21899,"journal":{"name":"Software: Practice and Experience","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135778964","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}
Summary Metamorphic testing (MT) is an effective technique to alleviate the test oracle problem. The principle of MT is to detect failures by checking whether some necessary properties, commonly known as metamorphic relations (MRs), of software under test (SUT) hold among multiple executions of source and follow‐up test cases. Since both the generation of follow‐up test cases and test result verification depend on MRs, the identification of MRs plays a key role in MT, which is an important yet difficult task requiring deep domain knowledge of the SUT. Accordingly, techniques that can direct a tester to identify MRs effectively are desirable. In this paper, we propose MT, a data mutation directed approach to identifying MRs. MT guides a tester to identify MRs by providing a set of data mutation operators and template‐style mapping rules, which not only alleviates the difficulties faced in the process of MR identification but also improves the identification effectiveness. We have further developed a tool to implement the proposed approach and conducted an empirical study to evaluate the MR identification effectiveness of MT and the performance of MRs identified by MT with respect to fault detection capability and statement coverage. The empirical results show that MT is able to identify MRs for numeric programs effectively, and the identified MRs have high fault detection capability and statement coverage. The work presented in this paper advances the field of MT by providing a simple yet practical approach to the MR identification problem.
{"title":"Identifying metamorphic relations: A data mutation directed approach","authors":"Chang‐ai Sun, Hui Jin, SiYi Wu, An Fu, ZuoYi Wang, Wing Kwong Chan","doi":"10.1002/spe.3280","DOIUrl":"https://doi.org/10.1002/spe.3280","url":null,"abstract":"Summary Metamorphic testing (MT) is an effective technique to alleviate the test oracle problem. The principle of MT is to detect failures by checking whether some necessary properties, commonly known as metamorphic relations (MRs), of software under test (SUT) hold among multiple executions of source and follow‐up test cases. Since both the generation of follow‐up test cases and test result verification depend on MRs, the identification of MRs plays a key role in MT, which is an important yet difficult task requiring deep domain knowledge of the SUT. Accordingly, techniques that can direct a tester to identify MRs effectively are desirable. In this paper, we propose MT, a data mutation directed approach to identifying MRs. MT guides a tester to identify MRs by providing a set of data mutation operators and template‐style mapping rules, which not only alleviates the difficulties faced in the process of MR identification but also improves the identification effectiveness. We have further developed a tool to implement the proposed approach and conducted an empirical study to evaluate the MR identification effectiveness of MT and the performance of MRs identified by MT with respect to fault detection capability and statement coverage. The empirical results show that MT is able to identify MRs for numeric programs effectively, and the identified MRs have high fault detection capability and statement coverage. The work presented in this paper advances the field of MT by providing a simple yet practical approach to the MR identification problem.","PeriodicalId":21899,"journal":{"name":"Software: Practice and Experience","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135884027","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}
Philipp Raith, Thomas Rausch, Alireza Furutanpey, Schahram Dustdar
Abstract This paper presents faas‐sim , a simulation framework tailored to serverless edge computing platforms. In serverless computing, platform operators are tasked with efficiently managing distributed computing infrastructure completely abstracted from application developers. To that end, platform operators and researchers need tools to design, build, and evaluate resource management techniques that efficiently use of infrastructure while optimizing application performance. This challenge is exacerbated in edge computing scenarios, where, compared to cloud computing, there is a lack of reference architectures, design tools, or standardized benchmarks. faas‐sim bridges this gap by providing (a) a generalized model of serverless systems that builds on the function‐as‐a‐service abstraction, (b) a simulator that uses trace data from real‐world edge computing testbeds and representative workloads, and (c) a network topology generator to model and simulate distributed and heterogeneous edge‐cloud systems. We present the conceptual design, implementation, and a thorough evaluation of faas‐sim . By running experiments on both real‐world test beds and replicating them using faas‐sim , we show that the simulator provides accurate results and reasonable simulation performance. We have profiled a wide range of edge computing infrastructure and workloads, focusing on typical edge computing scenarios such as edge AI inference or data processing. Moreover, we present several instances where we have successfully used faas‐sim to either design, optimize, or evaluate serverless edge computing systems.
{"title":"<i>faas‐sim</i>: A trace‐driven simulation framework for serverless edge computing platforms","authors":"Philipp Raith, Thomas Rausch, Alireza Furutanpey, Schahram Dustdar","doi":"10.1002/spe.3277","DOIUrl":"https://doi.org/10.1002/spe.3277","url":null,"abstract":"Abstract This paper presents faas‐sim , a simulation framework tailored to serverless edge computing platforms. In serverless computing, platform operators are tasked with efficiently managing distributed computing infrastructure completely abstracted from application developers. To that end, platform operators and researchers need tools to design, build, and evaluate resource management techniques that efficiently use of infrastructure while optimizing application performance. This challenge is exacerbated in edge computing scenarios, where, compared to cloud computing, there is a lack of reference architectures, design tools, or standardized benchmarks. faas‐sim bridges this gap by providing (a) a generalized model of serverless systems that builds on the function‐as‐a‐service abstraction, (b) a simulator that uses trace data from real‐world edge computing testbeds and representative workloads, and (c) a network topology generator to model and simulate distributed and heterogeneous edge‐cloud systems. We present the conceptual design, implementation, and a thorough evaluation of faas‐sim . By running experiments on both real‐world test beds and replicating them using faas‐sim , we show that the simulator provides accurate results and reasonable simulation performance. We have profiled a wide range of edge computing infrastructure and workloads, focusing on typical edge computing scenarios such as edge AI inference or data processing. Moreover, we present several instances where we have successfully used faas‐sim to either design, optimize, or evaluate serverless edge computing systems.","PeriodicalId":21899,"journal":{"name":"Software: Practice and Experience","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135883003","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}
Chenxing Zhong, He Zhang, Huang Huang, Zhikun Chen, Chao Li, Xiaodong Liu, Shanshan Li
Abstract As a predominant design method for microsservices architecture (MSA), domain‐driven design (DDD) utilizes a series of standard patterns in both models and implementations to effectively support the design of architectural elements. However, an implementation may deviate from its original domain model that uses certain patterns. The deviation between a domain model and its implementation is a type of architectural drift , which needs to be detected promptly. This paper proposes an approach, namely DOMICO, to check the conformance between the domain model and its implementation, by which the conformance is formalized by defining eight common structural patterns of domain modeling and their representations in both models and the corresponding source code. Based on the formalization, our approach can not only identify the discrepancies (e.g., divergence, absence, and modification) with respect to pattern elements, but also detect possible violations of 24 compliance rules imposed by the patterns. To validate DOMICO, we performed a case study to investigate its use in a supply chain project and its performance. The results show that DOMICO can accurately identify 100% inconsistency issues in the cases examined. As the first conformance checking approach for DDD, DOMICO can be integrated into the regular domain modeling process and help ensure the conformity of microservice implementations to models.
{"title":"DOMICO: Checking conformance between domain models and implementations","authors":"Chenxing Zhong, He Zhang, Huang Huang, Zhikun Chen, Chao Li, Xiaodong Liu, Shanshan Li","doi":"10.1002/spe.3272","DOIUrl":"https://doi.org/10.1002/spe.3272","url":null,"abstract":"Abstract As a predominant design method for microsservices architecture (MSA), domain‐driven design (DDD) utilizes a series of standard patterns in both models and implementations to effectively support the design of architectural elements. However, an implementation may deviate from its original domain model that uses certain patterns. The deviation between a domain model and its implementation is a type of architectural drift , which needs to be detected promptly. This paper proposes an approach, namely DOMICO, to check the conformance between the domain model and its implementation, by which the conformance is formalized by defining eight common structural patterns of domain modeling and their representations in both models and the corresponding source code. Based on the formalization, our approach can not only identify the discrepancies (e.g., divergence, absence, and modification) with respect to pattern elements, but also detect possible violations of 24 compliance rules imposed by the patterns. To validate DOMICO, we performed a case study to investigate its use in a supply chain project and its performance. The results show that DOMICO can accurately identify 100% inconsistency issues in the cases examined. As the first conformance checking approach for DDD, DOMICO can be integrated into the regular domain modeling process and help ensure the conformity of microservice implementations to models.","PeriodicalId":21899,"journal":{"name":"Software: Practice and Experience","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136184636","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}
Lauritz Thamsen, David Bermbach, Demetris Trihinas
As data analytics and machine learning increasingly permeate our cities, factories, and homes, the computing infrastructure for data-intensive systems becomes more challenging. That is, the vision of pervasive, intelligent, and cyber-physical IoT systems will not be realized with centralized cloud resources alone. Such resources are simply too far away from sensor-equipped devices and users, resulting in high latency, bandwidth bottlenecks, and unnecessary energy consumption. In addition, there are often privacy and security requirements that mandate distributed architectures. As a result, new distributed computing paradigms are emerging that promise to bring computing and storage closer to data sources and users. The emerging distributed computing environments of edge and fog computing provide additional resources within mobile networks, ISP infrastructure, and even LEO satellites. These diverse and dynamic computing environments pose significant challenges to the performance, dependability, and efficiency of data-intensive systems running on such infrastructure. At the same time, it is far less clear how to properly benchmark, evaluate, and test the behavior of systems that span IoT devices, edge nodes, and cloud resources. For example, IoT sensor data stream processing systems can be leveraged to continuously optimize the operation of urban infrastructures (such as public transportation systems, water networks, or medical infrastructures). The behavior of such systems must be thoroughly assessed before they can be deployed to edge and fog infrastructure. In addition, these systems must be evaluated reproducibly under the expected computing environment conditions, including variations of those conditions, given the inherently unsteady nature of IoT environments. In addition, there is growing concern about the energy consumption and greenhouse gas emissions of ICT (and especially distributed ML-based applications), which further warrants close examination of the behavior of new data-intensive applications. Despite significant research and development efforts to improve benchmarking, experimentation tools, and reproducible practices for data-intensive systems spanning from the edge to the cloud, more research is urgently needed. We therefore invited high-quality research papers on this topic for this special issue of Software: Practice and Experience, and we were able to select two out of four submissions for this special issue with the help of our reviewers. The first accepted paper is titled “faas-sim: A Trace-Driven Simulation Framework for Serverless Edge Computing Platforms”.1 It is co-authored by Philipp Raith, Thomas Rausch, Alireza Furutanpey, and Schahram Dustdar. The paper presents the design and implementation of a new simulation framework, “faas-sim,” for modeling and evaluating serverless software architectures spanning the edge-cloud continuum based on a scenario description, a given network topology, and workload traces. The new si
{"title":"Special Issue on benchmarking, experimentation tools, and reproducible practices for <scp>data‐intensive</scp> systems from edge to cloud","authors":"Lauritz Thamsen, David Bermbach, Demetris Trihinas","doi":"10.1002/spe.3282","DOIUrl":"https://doi.org/10.1002/spe.3282","url":null,"abstract":"As data analytics and machine learning increasingly permeate our cities, factories, and homes, the computing infrastructure for data-intensive systems becomes more challenging. That is, the vision of pervasive, intelligent, and cyber-physical IoT systems will not be realized with centralized cloud resources alone. Such resources are simply too far away from sensor-equipped devices and users, resulting in high latency, bandwidth bottlenecks, and unnecessary energy consumption. In addition, there are often privacy and security requirements that mandate distributed architectures. As a result, new distributed computing paradigms are emerging that promise to bring computing and storage closer to data sources and users. The emerging distributed computing environments of edge and fog computing provide additional resources within mobile networks, ISP infrastructure, and even LEO satellites. These diverse and dynamic computing environments pose significant challenges to the performance, dependability, and efficiency of data-intensive systems running on such infrastructure. At the same time, it is far less clear how to properly benchmark, evaluate, and test the behavior of systems that span IoT devices, edge nodes, and cloud resources. For example, IoT sensor data stream processing systems can be leveraged to continuously optimize the operation of urban infrastructures (such as public transportation systems, water networks, or medical infrastructures). The behavior of such systems must be thoroughly assessed before they can be deployed to edge and fog infrastructure. In addition, these systems must be evaluated reproducibly under the expected computing environment conditions, including variations of those conditions, given the inherently unsteady nature of IoT environments. In addition, there is growing concern about the energy consumption and greenhouse gas emissions of ICT (and especially distributed ML-based applications), which further warrants close examination of the behavior of new data-intensive applications. Despite significant research and development efforts to improve benchmarking, experimentation tools, and reproducible practices for data-intensive systems spanning from the edge to the cloud, more research is urgently needed. We therefore invited high-quality research papers on this topic for this special issue of Software: Practice and Experience, and we were able to select two out of four submissions for this special issue with the help of our reviewers. The first accepted paper is titled “faas-sim: A Trace-Driven Simulation Framework for Serverless Edge Computing Platforms”.1 It is co-authored by Philipp Raith, Thomas Rausch, Alireza Furutanpey, and Schahram Dustdar. The paper presents the design and implementation of a new simulation framework, “faas-sim,” for modeling and evaluating serverless software architectures spanning the edge-cloud continuum based on a scenario description, a given network topology, and workload traces. The new si","PeriodicalId":21899,"journal":{"name":"Software: Practice and Experience","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136210540","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}
Abstract Context The UML class diagram is commonly used to model functional structures and software code structures in both the preliminary and detailed design stages. And the abstraction level of UML class diagrams is usually higher than that of source code. Usually, there is a lack of trace links between these class diagrams and the source code, which may cause difficulties in understanding the source code, and affect the software evolution and maintenance. Objective The main goal of this article is to establish the trace links between highly abstracted UML class diagrams in the design phase and source code, and eventually help practitioners better understand source code. Method We propose an approach for the automated trace link establishment between UML class diagrams in the design phase and source code. To address the problem of abstraction level gap between them, we extend the UML class diagram by mining the synonymous phrases of class names and deducing the latent missing relationships between classes from multiple design documents. Then we build the trace links with a two‐phase approach including initial construction with fuzzy matching and further optimization by class relationship inference. Results Experiments on five open‐source projects show that the recalls of our approach are over 94%, and the F2‐scores are over 88%, with the gains of 30% to 60% than the four baselines. Conclusion Our work can be a reference for establishing the initial trace links between highly‐abstracted UML class diagrams and source code. Towards the higher abstraction of design diagrams, we extend UML class diagrams with the statistical analysis on multiple design documents. To guarantee the quality of trace links, we design a two‐phase approach by obtaining the “full but not good enough” trace links and filtering the “probably wrong” links. Experiments show that the main techniques of our approach behave as important role for tracing between high‐level class diagrams and source code.
{"title":"CDTC: Automatically establishing the trace links between class diagrams in design phase and source code","authors":"Fangwei Chen, Li Zhang, Xiaoli Lian","doi":"10.1002/spe.3270","DOIUrl":"https://doi.org/10.1002/spe.3270","url":null,"abstract":"Abstract Context The UML class diagram is commonly used to model functional structures and software code structures in both the preliminary and detailed design stages. And the abstraction level of UML class diagrams is usually higher than that of source code. Usually, there is a lack of trace links between these class diagrams and the source code, which may cause difficulties in understanding the source code, and affect the software evolution and maintenance. Objective The main goal of this article is to establish the trace links between highly abstracted UML class diagrams in the design phase and source code, and eventually help practitioners better understand source code. Method We propose an approach for the automated trace link establishment between UML class diagrams in the design phase and source code. To address the problem of abstraction level gap between them, we extend the UML class diagram by mining the synonymous phrases of class names and deducing the latent missing relationships between classes from multiple design documents. Then we build the trace links with a two‐phase approach including initial construction with fuzzy matching and further optimization by class relationship inference. Results Experiments on five open‐source projects show that the recalls of our approach are over 94%, and the F2‐scores are over 88%, with the gains of 30% to 60% than the four baselines. Conclusion Our work can be a reference for establishing the initial trace links between highly‐abstracted UML class diagrams and source code. Towards the higher abstraction of design diagrams, we extend UML class diagrams with the statistical analysis on multiple design documents. To guarantee the quality of trace links, we design a two‐phase approach by obtaining the “full but not good enough” trace links and filtering the “probably wrong” links. Experiments show that the main techniques of our approach behave as important role for tracing between high‐level class diagrams and source code.","PeriodicalId":21899,"journal":{"name":"Software: Practice and Experience","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135482642","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}
Sayed Mohsen Hashemi, Amir Sahafi, Amir Masoud Rahmani, Mahdi Bohlouli
Abstract The energy demand for Internet of Things (IoT) applications is increasing with a rise in IoT devices. Rising costs and energy demands can cause serious problems. Fog computing (FC) has recently emerged as a model for location‐aware tasks, data processing, fast computing, and energy consumption reduction. The Fog computing model assists cloud computing in fast processing at the network's edge, which also exerts a vital role in cloud computing. Due to the fast computing in fog servers, different quality of service (QoS) approaches have been proposed in various sections of the fog system, and several quality factors have been considered in this regard. Despite the significance of QoS in Fog computing, no extensive study has focused on QoS and energy consumption methods in this area. Therefore, this article investigates previous research on the use and guarantee of Fog computing. This article reviews six general approaches that discuss the published articles between 2015 and late May 2023. The focal point of this paper is evaluating Fog computing and the energy consumption strategy. This article further shows the advantages, disadvantages, tools, types of evaluation, and quality factors according to the selected approaches. Based on the reviewed studies, some open issues and challenges in Fog computing energy consumption management are suggested for further study.
{"title":"Energy‐aware resource management in fog computing for <scp>IoT</scp> applications: A review, taxonomy, and future directions","authors":"Sayed Mohsen Hashemi, Amir Sahafi, Amir Masoud Rahmani, Mahdi Bohlouli","doi":"10.1002/spe.3273","DOIUrl":"https://doi.org/10.1002/spe.3273","url":null,"abstract":"Abstract The energy demand for Internet of Things (IoT) applications is increasing with a rise in IoT devices. Rising costs and energy demands can cause serious problems. Fog computing (FC) has recently emerged as a model for location‐aware tasks, data processing, fast computing, and energy consumption reduction. The Fog computing model assists cloud computing in fast processing at the network's edge, which also exerts a vital role in cloud computing. Due to the fast computing in fog servers, different quality of service (QoS) approaches have been proposed in various sections of the fog system, and several quality factors have been considered in this regard. Despite the significance of QoS in Fog computing, no extensive study has focused on QoS and energy consumption methods in this area. Therefore, this article investigates previous research on the use and guarantee of Fog computing. This article reviews six general approaches that discuss the published articles between 2015 and late May 2023. The focal point of this paper is evaluating Fog computing and the energy consumption strategy. This article further shows the advantages, disadvantages, tools, types of evaluation, and quality factors according to the selected approaches. Based on the reviewed studies, some open issues and challenges in Fog computing energy consumption management are suggested for further study.","PeriodicalId":21899,"journal":{"name":"Software: Practice and Experience","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135406605","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}
Lulai Zhu, Damian Andrew Tamburri, Giuliano Casale
Abstract As the most successful realization of serverless, function as a service (FaaS) brings in a novel cloud computing paradigm that can save operating costs, reduce management effort, enable seamless scalability, and augment development productivity. Migration of an existing application to the serverless architecture is, however, an intricate task as a great number of decisions need to be made along the way. We propose in this paper RADF, a semi‐automatic approach that decomposes a monolith into serverless functions by analyzing the business logic inherent in the interface of the application. The proposed approach adopts a two‐stage refactoring strategy, where a coarse‐grained decomposition is performed at first, followed by a fine‐grained one. As such, the decomposition process is simplified into smaller steps and adaptable to generate a solution at either microservice or function level. We have implemented RADF in a holistic DevOps methodology and evaluated its capability for microservice identification and feasibility for code refactoring. In the evaluation experiments, RADF achieves lower coupling and relatively balanced cohesion, compared to previous decomposition approaches.
{"title":"RADF: Architecture decomposition for function as a service","authors":"Lulai Zhu, Damian Andrew Tamburri, Giuliano Casale","doi":"10.1002/spe.3276","DOIUrl":"https://doi.org/10.1002/spe.3276","url":null,"abstract":"Abstract As the most successful realization of serverless, function as a service (FaaS) brings in a novel cloud computing paradigm that can save operating costs, reduce management effort, enable seamless scalability, and augment development productivity. Migration of an existing application to the serverless architecture is, however, an intricate task as a great number of decisions need to be made along the way. We propose in this paper RADF, a semi‐automatic approach that decomposes a monolith into serverless functions by analyzing the business logic inherent in the interface of the application. The proposed approach adopts a two‐stage refactoring strategy, where a coarse‐grained decomposition is performed at first, followed by a fine‐grained one. As such, the decomposition process is simplified into smaller steps and adaptable to generate a solution at either microservice or function level. We have implemented RADF in a holistic DevOps methodology and evaluated its capability for microservice identification and feasibility for code refactoring. In the evaluation experiments, RADF achieves lower coupling and relatively balanced cohesion, compared to previous decomposition approaches.","PeriodicalId":21899,"journal":{"name":"Software: Practice and Experience","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135386129","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}
Abstract Software defect prediction (SDP) aims to build an effective prediction model for historical defect data from software repositories by some specialized techniques or algorithms, and predict the defect proneness of new software modules. Nevertheless, the complex internal intrinsic structure hidden behind the defect data makes it challenging for the built prediction model to capture the most expressive defect feature representations, and largely limits the SDP performance. Fortunately, artificial intelligence is interacting closely with humans and provides powerful intelligent technical support for addressing these SDP issues. In this article, we propose a robust intelligent SDP model called IMDAC based on deep learning and soft computing techniques. This model has three main advantages: (1) an effective deep generative network—InfoGAN (information maximizing GANs) is employed to conduct data augmentation, namely generating sufficient defect instances and achieving defect class balance simultaneously. (2) Select the fewest representative feature subset for the minimum error via an advanced multi‐objective optimization approach—MSEA (multi‐stage evolutionary algorithm). (3) Build a powerful end‐to‐end deep defect predictor by hybrid deep learning techniques—DAE (Denoising AutoEncoder) and CNN (convolutional neural network), which can not only reconstruct a clean “repaired” input with strong robustness and generalization capabilities via DAE, but also learn the abstract deep semantic features with strong discriminating capability via CNN. Experimental results verify the superiority and robustness of the IMDAC model across 15 software projects.
{"title":"IMDAC: A robust intelligent software defect prediction model via multi‐objective optimization and end‐to‐end hybrid deep learning networks","authors":"Kun Zhu, Nana Zhang, Changjun Jiang, Dandan Zhu","doi":"10.1002/spe.3274","DOIUrl":"https://doi.org/10.1002/spe.3274","url":null,"abstract":"Abstract Software defect prediction (SDP) aims to build an effective prediction model for historical defect data from software repositories by some specialized techniques or algorithms, and predict the defect proneness of new software modules. Nevertheless, the complex internal intrinsic structure hidden behind the defect data makes it challenging for the built prediction model to capture the most expressive defect feature representations, and largely limits the SDP performance. Fortunately, artificial intelligence is interacting closely with humans and provides powerful intelligent technical support for addressing these SDP issues. In this article, we propose a robust intelligent SDP model called IMDAC based on deep learning and soft computing techniques. This model has three main advantages: (1) an effective deep generative network—InfoGAN (information maximizing GANs) is employed to conduct data augmentation, namely generating sufficient defect instances and achieving defect class balance simultaneously. (2) Select the fewest representative feature subset for the minimum error via an advanced multi‐objective optimization approach—MSEA (multi‐stage evolutionary algorithm). (3) Build a powerful end‐to‐end deep defect predictor by hybrid deep learning techniques—DAE (Denoising AutoEncoder) and CNN (convolutional neural network), which can not only reconstruct a clean “repaired” input with strong robustness and generalization capabilities via DAE, but also learn the abstract deep semantic features with strong discriminating capability via CNN. Experimental results verify the superiority and robustness of the IMDAC model across 15 software projects.","PeriodicalId":21899,"journal":{"name":"Software: Practice and Experience","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135385382","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}
Philipp Wiesner, Marvin Steinke, Henrik Nickel, Yazan Kitana, Odej Kao
Abstract The growing electricity demand of IT infrastructure has raised significant concerns about its carbon footprint. To mitigate the associated emissions of computing systems, current efforts therefore increasingly focus on aligning the power usage of software with the availability of clean energy. To operate, such carbon‐aware applications require visibility and control over relevant metrics and configurations of the energy system. However, research and development of novel energy system abstraction layers and interfaces remain difficult due to the scarcity of available testing environments: Real testbeds are expensive to build and maintain, while existing simulation testbeds are unable to interact with real computing systems. To provide a widely applicable approach for developing and testing carbon‐aware software, we propose a method for integrating real applications into a simulated energy system through software‐in‐the‐loop simulation. The integration offers an API for accessing the energy system, while continuously modeling the computing system's power demand within the simulation. Our system allows for the integration of physical as well as virtual compute nodes, and can help accelerate research on carbon‐aware computing systems in the future.
{"title":"Software‐in‐the‐loop simulation for developing and testing carbon‐aware applications","authors":"Philipp Wiesner, Marvin Steinke, Henrik Nickel, Yazan Kitana, Odej Kao","doi":"10.1002/spe.3275","DOIUrl":"https://doi.org/10.1002/spe.3275","url":null,"abstract":"Abstract The growing electricity demand of IT infrastructure has raised significant concerns about its carbon footprint. To mitigate the associated emissions of computing systems, current efforts therefore increasingly focus on aligning the power usage of software with the availability of clean energy. To operate, such carbon‐aware applications require visibility and control over relevant metrics and configurations of the energy system. However, research and development of novel energy system abstraction layers and interfaces remain difficult due to the scarcity of available testing environments: Real testbeds are expensive to build and maintain, while existing simulation testbeds are unable to interact with real computing systems. To provide a widely applicable approach for developing and testing carbon‐aware software, we propose a method for integrating real applications into a simulated energy system through software‐in‐the‐loop simulation. The integration offers an API for accessing the energy system, while continuously modeling the computing system's power demand within the simulation. Our system allows for the integration of physical as well as virtual compute nodes, and can help accelerate research on carbon‐aware computing systems in the future.","PeriodicalId":21899,"journal":{"name":"Software: Practice and Experience","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135769111","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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