Pub Date : 2020-10-01DOI: 10.1109/ISSREW51248.2020.00093
Javier Alonso, K. Vaidyanathan, R. Pietrantuono
This paper summarizes the main methods adopted for the analysis and detection of software aging phenomena based on measurements (measurements-based aging analysis) as well as the metrics more commonly used as aging indicators.
本文总结了基于测量的软件老化现象分析和检测的主要方法(基于测量的老化分析)以及常用的老化指标。
{"title":"SAR Handbook Chapter: Measurements-based aging analysis","authors":"Javier Alonso, K. Vaidyanathan, R. Pietrantuono","doi":"10.1109/ISSREW51248.2020.00093","DOIUrl":"https://doi.org/10.1109/ISSREW51248.2020.00093","url":null,"abstract":"This paper summarizes the main methods adopted for the analysis and detection of software aging phenomena based on measurements (measurements-based aging analysis) as well as the metrics more commonly used as aging indicators.","PeriodicalId":202247,"journal":{"name":"2020 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128516889","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 : 2020-10-01DOI: 10.1109/ISSREW51248.2020.00039
Claudius V. Jordan, Florian Hauer, Philipp Foth, A. Pretschner
Testing is an important cost driver in development projects. Especially in the automotive industry, immense efforts are spent to carry out validation facing increasingly complex systems. Hardware-in-the-Loop test benches are essential elements for (functional) validation. Naturally, failures commonly occur, whose analysis is challenging, time-consuming and oftentimes performed manually, making the diagnosis process one decisive cost-driving factor. By experience, many failures happen due to few underlying faults. We discuss our lessons learned when performing similarity-based clustering to identify representative tests for each fault for system-level testing where test execution times are high and the complexity of the system-under-test and also the test setup leads to complicated failure conditions. Results from an industrial automotive case study–a drive train system dataset consisting of 57 test runs–show that utilizing our general, project-agnostic approach can effectively reduce failure analysis time even with a limited set of data points.
{"title":"Time-Series-Based Clustering for Failure Analysis in Hardware-in-the-Loop Setups: An Automotive Case Study","authors":"Claudius V. Jordan, Florian Hauer, Philipp Foth, A. Pretschner","doi":"10.1109/ISSREW51248.2020.00039","DOIUrl":"https://doi.org/10.1109/ISSREW51248.2020.00039","url":null,"abstract":"Testing is an important cost driver in development projects. Especially in the automotive industry, immense efforts are spent to carry out validation facing increasingly complex systems. Hardware-in-the-Loop test benches are essential elements for (functional) validation. Naturally, failures commonly occur, whose analysis is challenging, time-consuming and oftentimes performed manually, making the diagnosis process one decisive cost-driving factor. By experience, many failures happen due to few underlying faults. We discuss our lessons learned when performing similarity-based clustering to identify representative tests for each fault for system-level testing where test execution times are high and the complexity of the system-under-test and also the test setup leads to complicated failure conditions. Results from an industrial automotive case study–a drive train system dataset consisting of 57 test runs–show that utilizing our general, project-agnostic approach can effectively reduce failure analysis time even with a limited set of data points.","PeriodicalId":202247,"journal":{"name":"2020 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126061447","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 : 2020-10-01DOI: 10.1109/ISSREW51248.2020.00035
Maninder Singh, G. Walia
Software requirement specification (SRS) documents are written in natural language (NL) and are prone to contain faults due to the inherently ambiguous nature of NL. Inspections are employed to find and fix these faults during the early phases of development, where these are the most cost-effective to fix. Inspections being too manual are very tedious and time consuming to perform. After fixing a fault, the SRS author has to manually re-inspect the document to make sure if there are other similar requirements that need a fix, and also if fixing a fault does not reintroduce another fault in the document (i.e., change impact analysis). The proposed approach in this paper employs NL processing, machine learning, semantic analysis, and graph mining approaches to generate a graph of inter-related requirements (IRR) based on semantic similarity score. The IRR graph is next mined using graph mining approaches to analyze the impact of a change. Our approach when applied using a real SRS generated IRR and yielded promising results. Graph mining approaches resulted in a G-mean of more than 90% to accurately identify the highly similar requirements to support the CIA.
{"title":"Using Semantic Analysis and Graph Mining Approaches to Support Software Fault Fixation","authors":"Maninder Singh, G. Walia","doi":"10.1109/ISSREW51248.2020.00035","DOIUrl":"https://doi.org/10.1109/ISSREW51248.2020.00035","url":null,"abstract":"Software requirement specification (SRS) documents are written in natural language (NL) and are prone to contain faults due to the inherently ambiguous nature of NL. Inspections are employed to find and fix these faults during the early phases of development, where these are the most cost-effective to fix. Inspections being too manual are very tedious and time consuming to perform. After fixing a fault, the SRS author has to manually re-inspect the document to make sure if there are other similar requirements that need a fix, and also if fixing a fault does not reintroduce another fault in the document (i.e., change impact analysis). The proposed approach in this paper employs NL processing, machine learning, semantic analysis, and graph mining approaches to generate a graph of inter-related requirements (IRR) based on semantic similarity score. The IRR graph is next mined using graph mining approaches to analyze the impact of a change. Our approach when applied using a real SRS generated IRR and yielded promising results. Graph mining approaches resulted in a G-mean of more than 90% to accurately identify the highly similar requirements to support the CIA.","PeriodicalId":202247,"journal":{"name":"2020 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116219644","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 : 2020-10-01DOI: 10.1109/ISSREW51248.2020.00058
Alexandra Figueiredo, Tatjana Lide, M. Correia
Most web applications are compromised due to vulnerable source code [1]. Static code analysis tools that are often used to find security vulnerabilities in code have two main problems: they are language-specific, and they have to be programmed, or at least configured manually, to deal with new types of vulnerabilities.
{"title":"Multi-Language Web Vulnerability Detection","authors":"Alexandra Figueiredo, Tatjana Lide, M. Correia","doi":"10.1109/ISSREW51248.2020.00058","DOIUrl":"https://doi.org/10.1109/ISSREW51248.2020.00058","url":null,"abstract":"Most web applications are compromised due to vulnerable source code [1]. Static code analysis tools that are often used to find security vulnerabilities in code have two main problems: they are language-specific, and they have to be programmed, or at least configured manually, to deal with new types of vulnerabilities.","PeriodicalId":202247,"journal":{"name":"2020 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134381823","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 : 2020-10-01DOI: 10.1109/ISSREW51248.2020.00033
Monika Jaskolka, Stephen Scott, Vera Pantelic, Alan Wassyng, M. Lawford
Modular decomposition is widely used in software engineering to support design, testing and maintenance of software intensive systems. Model-Based Development (MBD) is a paradigm for developing complex software systems using graphical approaches, with MathWorks’ Simulink being a popular choice. How to develop modular Simulink models with stable interfaces, that facilitate understanding and testing, and achieve low coupling and high cohesion, is relatively understudied. This paper applies a new modular decomposition approach to Simulink case studies from the aerospace and nuclear domains. We evaluate how well it supports information hiding, and its impact on coupling and cohesion, interface complexity, cyclomatic complexity, testability, and performance.
{"title":"Applying Modular Decomposition in Simulink","authors":"Monika Jaskolka, Stephen Scott, Vera Pantelic, Alan Wassyng, M. Lawford","doi":"10.1109/ISSREW51248.2020.00033","DOIUrl":"https://doi.org/10.1109/ISSREW51248.2020.00033","url":null,"abstract":"Modular decomposition is widely used in software engineering to support design, testing and maintenance of software intensive systems. Model-Based Development (MBD) is a paradigm for developing complex software systems using graphical approaches, with MathWorks’ Simulink being a popular choice. How to develop modular Simulink models with stable interfaces, that facilitate understanding and testing, and achieve low coupling and high cohesion, is relatively understudied. This paper applies a new modular decomposition approach to Simulink case studies from the aerospace and nuclear domains. We evaluate how well it supports information hiding, and its impact on coupling and cohesion, interface complexity, cyclomatic complexity, testability, and performance.","PeriodicalId":202247,"journal":{"name":"2020 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW)","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132580839","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 : 2020-10-01DOI: 10.1109/ISSREW51248.2020.00099
E. Andrade, F. Machida, R. Pietrantuono, Domenico Cotroneo
Image classification systems using machine learning are rapidly adopted in many software application systems. Machine learning models built for image classification tasks are usually deployed on either cloud computing or edge computers close to data sources depending on the performance and resource requirements. However, software reliability aspects during the operation of these systems have not been properly explored. In this paper, we experimentally investigate the software aging phenomena in image classification systems that are continuously running on cloud or edge computing environments. By performing statistical analysis on the measurement data, we detected a suspicious phenomenon of software aging induced by image classification workloads in the memory usages for cloud and edge computing systems. Contrary to the expectation, our experimental results show that the edge system is less impacted by software aging than the cloud system that has four times larger allocated memory resources. We also disclose our software aging data set on our project web site for further exploration of software aging and rejuvenation research.
{"title":"Software Aging in Image Classification Systems on Cloud and Edge","authors":"E. Andrade, F. Machida, R. Pietrantuono, Domenico Cotroneo","doi":"10.1109/ISSREW51248.2020.00099","DOIUrl":"https://doi.org/10.1109/ISSREW51248.2020.00099","url":null,"abstract":"Image classification systems using machine learning are rapidly adopted in many software application systems. Machine learning models built for image classification tasks are usually deployed on either cloud computing or edge computers close to data sources depending on the performance and resource requirements. However, software reliability aspects during the operation of these systems have not been properly explored. In this paper, we experimentally investigate the software aging phenomena in image classification systems that are continuously running on cloud or edge computing environments. By performing statistical analysis on the measurement data, we detected a suspicious phenomenon of software aging induced by image classification workloads in the memory usages for cloud and edge computing systems. Contrary to the expectation, our experimental results show that the edge system is less impacted by software aging than the cloud system that has four times larger allocated memory resources. We also disclose our software aging data set on our project web site for further exploration of software aging and rejuvenation research.","PeriodicalId":202247,"journal":{"name":"2020 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW)","volume":"110-111 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132827829","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 : 2020-10-01DOI: 10.1109/ISSREW51248.2020.00031
L. Jagadeesan, V. Mendiratta
Modern application development and deployment is rapidly evolving to microservices based architectures, in which thousands of microservices communicate with one another and can be independently scaled and updated. While these architectures enable flexibility of deployment and frequency of upgrades, the naive use of thousands of communicating and frequently updated microservices can significantly impact the reliability of applications. To address these challenges, service meshes are used to rapidly detect and respond to microservices failures without necessitating changes to the microservices themselves. However, there are inherent tradeoffs that service meshes must make with regards to how quickly they assume a microservice has failed and the subsequent impact on overall application reliability. We present in this paper a modeling framework for microservices and service mesh reliability that takes these tradeoffs into account. Index Terms–microservices, service mesh, sidecars, circuit breakers, reliability, availability, resilience, reliability models, probabilistic model checking, PRISM.
{"title":"When Failure is (Not) an Option: Reliability Models for Microservices Architectures","authors":"L. Jagadeesan, V. Mendiratta","doi":"10.1109/ISSREW51248.2020.00031","DOIUrl":"https://doi.org/10.1109/ISSREW51248.2020.00031","url":null,"abstract":"Modern application development and deployment is rapidly evolving to microservices based architectures, in which thousands of microservices communicate with one another and can be independently scaled and updated. While these architectures enable flexibility of deployment and frequency of upgrades, the naive use of thousands of communicating and frequently updated microservices can significantly impact the reliability of applications. To address these challenges, service meshes are used to rapidly detect and respond to microservices failures without necessitating changes to the microservices themselves. However, there are inherent tradeoffs that service meshes must make with regards to how quickly they assume a microservice has failed and the subsequent impact on overall application reliability. We present in this paper a modeling framework for microservices and service mesh reliability that takes these tradeoffs into account. Index Terms–microservices, service mesh, sidecars, circuit breakers, reliability, availability, resilience, reliability models, probabilistic model checking, PRISM.","PeriodicalId":202247,"journal":{"name":"2020 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW)","volume":"790 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116135290","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 : 2020-10-01DOI: 10.1109/ISSREW51248.2020.00053
Thomas Hirsch
Fault localization has been determined as a major resource factor in the software development life cycle. Academic fault localization techniques are mostly unknown and unused in professional environments. Although manual debugging approaches can vary significantly depending on bug type (e.g. memory bugs or semantic bugs), these differences are not reflected in most existing fault localization tools. Little research has gone into automated identification of bug types to optimize the fault localization process. Further, existing fault localization techniques leverage on historical data only for augmentation of suspiciousness rankings. This thesis aims to provide a fault localization framework by combining data from various sources to help developers in the fault localization process. To achieve this, a bug classification schema is introduced, benchmarks are created, and a novel fault localization method based on historical data is proposed.
{"title":"A Fault Localization and Debugging Support Framework driven by Bug Tracking Data","authors":"Thomas Hirsch","doi":"10.1109/ISSREW51248.2020.00053","DOIUrl":"https://doi.org/10.1109/ISSREW51248.2020.00053","url":null,"abstract":"Fault localization has been determined as a major resource factor in the software development life cycle. Academic fault localization techniques are mostly unknown and unused in professional environments. Although manual debugging approaches can vary significantly depending on bug type (e.g. memory bugs or semantic bugs), these differences are not reflected in most existing fault localization tools. Little research has gone into automated identification of bug types to optimize the fault localization process. Further, existing fault localization techniques leverage on historical data only for augmentation of suspiciousness rankings. This thesis aims to provide a fault localization framework by combining data from various sources to help developers in the fault localization process. To achieve this, a bug classification schema is introduced, benchmarks are created, and a novel fault localization method based on historical data is proposed.","PeriodicalId":202247,"journal":{"name":"2020 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121928064","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 : 2020-10-01DOI: 10.1109/issrew51248.2020.00016
{"title":"Message from the WoSAR 2020 Workshop Chairs","authors":"","doi":"10.1109/issrew51248.2020.00016","DOIUrl":"https://doi.org/10.1109/issrew51248.2020.00016","url":null,"abstract":"","PeriodicalId":202247,"journal":{"name":"2020 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125039225","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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