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Fault propagation analysis is a process used to determine the consequences of faults residing in a computer system. A typical computer system consists of diverse components (e.g., electronic and software components), thus, the faults contained in these components tend to possess diverse characteristics. How to describe and model such diverse faults, and further determine fault propagation through different components are challenging problems to be addressed in the fault propagation analysis. This paper proposes an ontology-based approach, which is an integrated method allowing for the generation, injection, and propagation through inference of diverse faults at an early stage of the design of a computer system. The results generated by the proposed framework can verify system robustness and identify safety and reliability risks with limited design level information. In this paper, we propose an ontological framework and its application to analyze an example safety-critical computer system. The analysis result shows that the proposed framework is capable of inferring fault propagation paths through software and hardware components and is effective in predicting the impact of faults.

Software defect prevention is an important way to reduce the defect introduction rate. As the primary cause of software defects, human error can be the key to understanding and preventing software defects. This paper proposes a defect prevention approach based on human error mechanisms: DPeHE. The approach includes both knowledge and regulation training in human error prevention. Knowledge training provides programmers with explicit knowledge on why programmers commit errors, what kinds of errors tend to be committed under different circumstances, and how these errors can be prevented. Regulation training further helps programmers to promote the awareness and ability to prevent human errors through practice. The practice is facilitated by a problem solving checklist and a root cause identification checklist. This paper provides a systematic framework that integrates knowledge across disciplines, e.g., cognitive science, software psychology and software engineering to defend against human errors in software development. Furthermore, we applied this approach in an international company at CMM Level 5 and a software development institution at CMM Level 1 in the Chinese Aviation Industry. The application cases show that the approach is feasible and effective in promoting developers’ ability to prevent software defects, independent of process maturity levels.

Software quality encompasses many characteristics. Within limited resources, how to select the most important characteristics for quality assessment remains an important question. This paper designed a survey to prioritize the goals and software quality characteristics in safety-critical domains. The data were collected from 34 practitioners from 18 organizations in the Chinese safety-critical industries. The results provide guidance on better allocating resources to software quality assessment in safety-critical domains.

Software dependability engineering is an interdisciplinary area built on numerous concepts with complex interrelations. Understanding the relations between various concepts and integrating such conceptual knowledge is fundamental for educational, research and industrial purposes. This paper proposes a method to merge experts' conceptual knowledge represented in the form of Causal Mechanism Graphs (CMG). A case study was conducted to apply the method on 14 causal mechanism graphs obtained from 11 domain experts. The obtained consensus knowledge was then validated and evolved based on another 24 experts' opinions. The results demonstrate the main causal mechanisms that influence software dependability attributes, i.e. software aspect of reliability, safety, security, availability and maintainability. The application shows that the CMG merging method has the advantage of explicitly aggregating complex causal knowledge without losing information on the original knowledge structure. The application also shows that the CMG merging method is capable to integrate various factors that influence multiple dependability attributes at different stages of the software development lifecycle.

With the increasing dependence on digital instrumentation and control (I&C) systems in nuclear power plants, software has become a significant determinant of system safety assurance. To expand the use of digital technology in the nuclear industry, systematic methods are required for quantifying the safety of software-based I&C systems in safety critical applications. A software safety quantification model limited to the requirements phase is built in this paper based on the causal mechanisms that challenge safety. A preliminary mathematical method was developed to assess the number of requirements faults and their sub-types. A case study is conducted on a function of a reactor protection system to verify the validity of the quantification model.

Residual defects at the time of delivery are an important concern for safety critical software systems. Suppliers and customers are urged to get evidence for what they can do to reduce residual defects. Thus, it is meaningful to learn from historical data concerning the kinds of defects that have escaped from the existing quality assurance approaches and the factors that lead to the residual defects. A total of 3747 defects from 70 software systems developed by 29 Chinese aviation organizations were collected from acceptance tests during the last 5years. For all these organizations, 38 domain experts from the industry assessed the process consistency to the standard built in the framework of Capability Maturity Model CMM. Results demonstrate that the process improvement in the range of high consistency is effective in reducing total defects, as well as the minor and severe defects. The high consistency adoption of the practices in CMM Level 1 to Level 3 is more effective in reducing minor defects than severe defects. Causal analysis was performed to investigate the underlying mechanisms. Results reveal that individual cognitive failures cause 87% of severe defects. More approaches to help software developers manage their interior cognitive process are needed for improving software quality in the future. Copyright © 2015 John Wiley & Sons, Ltd.

Understanding the trend of software defect density at the time of delivery has significant implications for organizations or agencies to make long-term risk control strategies. Software development is a knowledge-intensive activity. Organizations and software developers are expected to reduce defects rates with the accumulation of knowledge and experience, however, they are constantly confronted with challenges imposed by new projects. This paper investigates the compound effects of knowledge experience and new projects on software defects. The study performs an empirical analysis on 60 software systems developed by 8 organizations in the Chinese aviation industry from year 2007 to 2014. The results show that the density of severe defects obviously reduces over time, but no consistent trend is observed for defects of low severity. Moreover, the density of high severity defects acutely increases in newly developed projects. This reveals that enhanced strategies should be employed to reduce severe defects in new software projects, in addition to the current practices of software process improvement.

Software diversity is known to improve fault tolerance in N-version software systems by independent development. As the leading cause of software faults, human error is considered an important factor in diversity seeking. However, there is little scientific research focusing on how to seek software fault diversity based on human error mechanisms. A literature review was conducted to extract factors that may differentiate people with respect to human error-proneness. In addition, we constructed a conceptual model of the links between human error diversity and software diversity. An experiment was designed to validate the hypotheses, in the form of a programming contest, accompanied by a survey of cognitive styles and personality traits. One hundred ninety-two programs were submitted for the identical problem, and 70 surveys were collected. Code inspection revealed 23 faults, of which 10 were coincident faults. The results show that personality traits seems not effective predictors for fault diversity as a whole model, whereas cognitive styles and program measurements moderately account for the variation of fault density. The results also show causal relations between performance levels and coincident faults: coincident faults are unlikely to occur at skill-based performance level; the coincident faults introduced in rule-based performances show a high probability of occurrence, and the coincident faults introduced in knowledge-based performances are shaped by the content and formats of the task itself. Based on these results, we have proposed a model to seek software diversity and prevent coincident faults.