Risk-based testing is a frequently used testing approach which utilizes identified risks of a sof... more Risk-based testing is a frequently used testing approach which utilizes identified risks of a software system to provide decision support in all phases of the testing process. Risk assessment, which is a core activity of every risk-based testing process, is often done in an ad-hoc manual way. Software quality assessments based on quality models already describe the product-related risks of a whole software product and provide objective and automation-supported assessments. But so far, quality models have not been applied for risk assessment and risk-based testing in a systematic way. This article tries to fill this gap and investigates how the information and data of a quality assessment based on the open quality model QuaMoCo can be integrated into risk-based testing. We first present two generic approaches how quality assessments based on quality models can be integrated into risk-based testing and then provide the concrete integration on the basis of the open quality model QuaMoCo. Based on five open source products, a case study is performed. Results of the case study show that a risk-based testing strategy outperforms a lines of code-based testing strategy with regards to the number of defects detected. Moreover, a significant positive relationship between the risk coefficient and the associated number of defects was found.
By promising huge benefits for industries and new opportunities for a multitude of applications, ... more By promising huge benefits for industries and new opportunities for a multitude of applications, Industry 4.0 is currently one of the major and most discussed topics in academia and practice. Beside this trend, today's manufacturing companies have to produce products of highest quality in order to retain competitive and satisfy the steadily increasing customer requirements. Thus, an essential prerequisite and key to sustainable economical success for any company is to focus on quality management. Through its concepts (Smart Factory, Cyber-Physical System, Internet of Things and Services), Industry 4.0 provides promising opportunities for quality management. Therefore, this paper presents research challenges of Industry 4.0 for quality management motivated by a practical insight of an Austrian electronic manufacturing services company. The presented research challenges are structured by the three key aspects of Industry 4.0 (vertical, horizontal and end-to-end engineering integration) and grounded on the DIN ISO 9000 quality management systems approach.
With the increasing importance and complexity of Internet of Things (IoT) applications, also the ... more With the increasing importance and complexity of Internet of Things (IoT) applications, also the development of adequate quality assurance techniques becomes essential. Due to the massive amount of data generated in workflows of IoT applications, data science plays a key role in their quality assurance. In this paper, we present respective data science challenges to improve quality assurance of Internet of Things applications. Based on an informal literature review, we first outline quality assurance requirements evolving with the IoT grouped into six categories (Environment, User, Compliance/Service Level Agreement, Organizational, Security and Data Management). Finally, we present data science challenges to improve the quality assurance of Internet of Things applications subdivided into four categories (Defect prevention, Defect analysis, User incorporation and Organizational) derived from the six quality assurance requirement categories.
Risk-based testing is a frequently used testing approach which utilizes identified risks of a sof... more Risk-based testing is a frequently used testing approach which utilizes identified risks of a software system to provide decision support in all phases of the testing process. Risk assessment, which is a core activity of every risk-based testing process, is often done in an ad-hoc manual way. Software quality assessments based on quality models already describe the product-related risks of a whole software product and provide objective and automation-supported assessments. But so far, quality models have not been applied for risk assessment and risk-based testing in a systematic way. This article tries to fill this gap and investigates how the information and data of a quality assessment based on the open quality model QuaMoCo can be integrated into risk-based testing. We first present two generic approaches how quality assessments based on quality models can be integrated into risk-based testing and then provide the concrete integration on the basis of the open quality model QuaMoCo. Based on five open source products, a case study is performed. Results of the case study show that a risk-based testing strategy outperforms a lines of code-based testing strategy with regards to the number of defects detected. Moreover, a significant positive relationship between the risk coefficient and the associated number of defects was found.
By promising huge benefits for industries and new opportunities for a multitude of applications, ... more By promising huge benefits for industries and new opportunities for a multitude of applications, Industry 4.0 is currently one of the major and most discussed topics in academia and practice. Beside this trend, today's manufacturing companies have to produce products of highest quality in order to retain competitive and satisfy the steadily increasing customer requirements. Thus, an essential prerequisite and key to sustainable economical success for any company is to focus on quality management. Through its concepts (Smart Factory, Cyber-Physical System, Internet of Things and Services), Industry 4.0 provides promising opportunities for quality management. Therefore, this paper presents research challenges of Industry 4.0 for quality management motivated by a practical insight of an Austrian electronic manufacturing services company. The presented research challenges are structured by the three key aspects of Industry 4.0 (vertical, horizontal and end-to-end engineering integration) and grounded on the DIN ISO 9000 quality management systems approach.
With the increasing importance and complexity of Internet of Things (IoT) applications, also the ... more With the increasing importance and complexity of Internet of Things (IoT) applications, also the development of adequate quality assurance techniques becomes essential. Due to the massive amount of data generated in workflows of IoT applications, data science plays a key role in their quality assurance. In this paper, we present respective data science challenges to improve quality assurance of Internet of Things applications. Based on an informal literature review, we first outline quality assurance requirements evolving with the IoT grouped into six categories (Environment, User, Compliance/Service Level Agreement, Organizational, Security and Data Management). Finally, we present data science challenges to improve the quality assurance of Internet of Things applications subdivided into four categories (Defect prevention, Defect analysis, User incorporation and Organizational) derived from the six quality assurance requirement categories.
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