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Developing Safety-Critical Software
As the complexity and criticality of software increase and projects are pressed to develop software faster and more cheaply, it becomes even more important to ensure that software-intensive systems are reliable and safe. This book helps you develop, manage, and approve safety-critical software more efficiently and effectively. Although the focus is on aviation software and compliance with RTCA/DO-178C and its supplements, the principles also apply to other safety-critical software. Written by an international authority on the subject, this book brings you a wealth of best practices, real-world examples, and concrete recommendations.
eBook
Bugs in machine learning-based systems: a faultload benchmark
The rapid escalation of applying Machine Learning (ML) in various domains has led to paying more attention to the quality of ML components. There is then a growth of techniques and tools aiming at improving the quality of ML components and integrating them into the ML-based system safely. Although most of these tools use bugs’ lifecycle, there is no standard benchmark of bugs to assess their performance, compare them and discuss their advantages and weaknesses. In this study, we firstly investigate the reproducibility and verifiability of the bugs in ML-based systems and show the most important factors in each one. Then, we explore the challenges of generating a benchmark of bugs in ML-based software systems and provide a bug benchmark namely defect4ML that satisfies all criteria of standard benchmark, i.e. relevance, reproducibility, fairness, verifiability, and usability. This faultload benchmark contains 100 bugs reported by ML developers in GitHub and Stack Overflow, using two of the most popular ML frameworks: TensorFlow and Keras. defect4ML also addresses important challenges in Software Reliability Engineering of ML-based software systems, like: 1) fast changes in frameworks, by providing various bugs for different versions of frameworks, 2) code portability, by delivering similar bugs in different ML frameworks, 3) bug reproducibility, by providing fully reproducible bugs with complete information about required dependencies and data, and 4) lack of detailed information on bugs, by presenting links to the bugs’ origins. defect4ML can be of interest to ML-based systems practitioners and researchers to assess their testing tools and techniques.
Journal Article
A software reliability model incorporating martingale process with gamma-distributed environmental factors
As the increasing application of software system in various industry, software reliability gains more attention from the researchers and practitioners in the past few decades. The goal of such an expanding application of software system is to continuously bring convenience and functionality in everyday life. Lots of environmental factors defined by many studies may have positive/negative impact on software reliability during the development process (Zhu et al. in J Syst Softw 109:150–160, 2015; Clarke and O’Connor in Inf Softw Technol 54(5):433–447, 2012; Zhu and Pham in J Syst Softw 1–18, 2017b). However, most existing software reliability models have not incorporated these environmental factors in the model consideration. In this paper, we propose a theoretic software reliability model incorporating the fault detection process is a stochastic process due to the randomness caused by the environmental factors. The environmental factor, Percentage of Reused Modules, is described as a gamma distribution in this study based on the collected data from industry. Open Source Software project data are included to demonstrate the effectiveness and predictive power of the proposed model.
Journal Article
Software for dependable systems : sufficient evidence?
The focus of Software for Dependable Systemsis a set of fundamental principles that underlie software system dependability and that suggest a different approach to the development and assessment of dependable software.Unfortunately, it is difficult to assess the dependability of software.
eBook
A generalized multiple environmental factors software reliability model with stochastic fault detection process
Software systems have been widely applied in numerous safety–critical domains; however, large-scale software development is still considered as a complicated and expensive activity. As the latest trends in software industry accelerate the complexity and dependency of software development, such complicated and human-centered process needs to be addressed well. Meanwhile, recent survey investigations (Zhu et al. in J Syst Softw 109:150–160, 2015; Zhu and Pham in J Syst Softw 132:72–84, 2017) revealed that environmental factors, defined from software development, have significant impacts on software reliability. Considering such significant impacts, we first propose a generalized multiple-environmental-factors software reliability growth model with multiple environmental factors and the associated randomness under the martingale framework. The randomness is reflected on the process of detecting software fault. Indeed, this is a stochastic fault detection process. As an illustration, a specific multiple-environmental-factors software reliability growth model incorporating two specific environmental factors, percentage of reused modules and frequency of program specification change, is further developed. Lastly, we employ two real-world data sets to demonstrate the prediction performance of the proposed generalized multiple-environmental-factors software reliability growth model.
Journal Article
Multi release software reliability modelling incorporating fault generation in detection process and fault dependency with change point in correction process
Many software reliability growth models (SRGMs) have been proposed in the literature to evaluate the remaining faults and software reliability. The probability of getting failure-free software within a specified period and environment is known as software reliability and is recognized as one of the essential aspects. In this paper, we present a new SRGM of the fault detection process (FDP) and fault correction process (FCP) and study the dependency between the FDP and FCP as the amount of fault dependency, not time dependency. The FDP is modeled by considering a multi-release concept where the leftover faults from the previous release and newly added faults after enhancing the existing features in the software are considered. Further, the FCP model is proposed by introducing the change point concept in the fault dependency function. These models are validated on two actual medium-sized software system data sets. The results show that the proposed models fit the data set more accurately than the existing SRGMs. We have also discussed the optimal release time through a cost model where setup, testing, and debugging costs are introduced in both the testing and operational phases in the cost model.
Journal Article