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"Software reliability"
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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
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
Software for dependable systems : sufficient evidence?
The focus of Software for Dependable Systems is 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. The field of software engineering suffers from a pervasive lack of evidence about the incidence and severity of software failures; about the dependability of existing software systems; about the efficacy of existing and proposed development methods; about the benefits of certification schemes; and so on. There are many anecdotal reports, which-although often useful for indicating areas of concern or highlighting promising avenues of research-do little to establish a sound and complete basis for making policy decisions regarding dependability. The committee regards claims of extraordinary dependability that are sometimes made on this basis for the most critical of systems as unsubstantiated, and perhaps irresponsible. This difficulty regarding the lack of evidence for system dependability leads to two conclusions: (1) that better evidence is needed, so that approaches aimed at improving the dependability of software can be objectively assessed, and (2) that, for now, the pursuit of dependability in software systems should focus on the construction and evaluation of evidence.The committee also recognized the importance of adopting the practices that are already known and used by the best developers; this report gives a sample of such practices. Some of these (such as systematic configuration management and automated regression testing) are relatively easy to adopt; others (such as constructing hazard analyses and threat models, exploiting formal notations when appropriate, and applying static analysis to code) will require new training for many developers. However valuable, though, these practices are in themselves no silver bullet, and new techniques and methods will be required in order to build future software systems to the level of dependability that will be required.
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
A multi-release software reliability modeling for open source software incorporating dependent fault detection process
The increasing dependence of our modern society on software systems has driven the development of software products become even more competitive and time-consuming. Single release software product no longer meets the increasing market requirements. Thereby it is important to release multiple version software products in order to add new features in the next release and fix remaining faults from previous release. In this paper, we develop a multi-release software reliability model with consideration of the remaining software faults from previous release and the new introduced-faults (from newly added features). Additionally, dependent fault detection process is taken into account in this research. In particular, the detection of a new fault for developing the next release depends on the detection of the remaining faults from previous release and the detection of the new introduced-faults. The proposed model is validated on the open source software project datasets with multiple releases.
Journal Article
Software reliability prediction and release time management with coverage
PurposeSoftware testing is needed to produce extremely reliable software products. A crucial decision problem that the software developer encounters is to ascertain when to terminate the testing process and when to release the software system in the market. With the growing need to deliver quality software, the critical assessment of reliability, cost of testing and release time strategy is requisite for project managers. This study seeks to examine the reliability of the software system by proposing a generalized testing coverage-based software reliability growth model (SRGM) that incorporates the effect of testing efforts and change point. Moreover, the strategic software time-to-market policy based on costreliability criteria is suggested.Design/methodology/approachThe fault detection process is modeled as a composite function of testing coverage, testing efforts and the continuation time of the testing process. Also, to assimilate factual scenarios, the current research exhibits the influence of software users refer as reporters in the fault detection process. Thus, this study models the reliability growth phenomenon by integrating the number of reporters and the number of instructions executed in the field environment. Besides, it is presumed that the managers release the software early to capture maximum market share and continue the testing process for an added period in the user environment. The multiattribute utility theory (MAUT) is applied to solve the optimization model with release time and testing termination time as two decision variables.FindingsThe practical applicability and performance of the proposed methodology are demonstrated through real-life software failure data. The findings of the empirical analysis have shown the superiority of the present study as compared to conventional approaches.Originality/valueThis study is the first attempt to assimilate testing coverage phenomenon in joint optimization of software time to market and testing duration.
Journal Article