Automated Code Generation for Industrial-Strength Systems

Model-driven engineering proposes to develop software systems by first creating an executable model of the system design and then transforming this model into an implementation. This paper discusses the design of an automatic code generation system that transforms such models into product implementations for highly reliable, industrial-strength systems. It provides insights, practical considerations, and lessons learned when developing code generators for applications that must conform to the constraints imposed by real-world, high-performance systems. Automatic code generation has played a large part in dramatically increasing both the quality and the reliability of software for these systems

Software quality and reliability prediction using Dempster -Shafer theory

As software systems are increasingly deployed in mission critical applications, accurate quality and reliability predictions are becoming a necessity. Most accurate prediction models require extensive testing effort, implying increased cost and slowing down the development life cycle. We developed two novel statistical models based on Dempster-Shafer theory, which provide accurate predictions from relatively small data sets of direct and indirect software reliability and quality predictors. The models are flexible enough to incorporate information generated throughout the development life-cycle to improve the prediction accuracy.;Our first contribution is an original algorithm for building Dempster-Shafer Belief Networks using prediction logic. This model has been applied to software quality prediction. We demonstrated that the prediction accuracy of Dempster-Shafer Belief Networks is higher than that achieved by logistic regression, discriminant analysis, random forests, as well as the algorithms in two machine learning software packages, See5 and WEKA. The difference in the performance of the Dempster-Shafer Belief Networks over the other methods is statistically significant.;Our second contribution is also based on a practical extension of Dempster-Shafer theory. The major limitation of the Dempsters rule and other known rules of evidence combination is the inability to handle information coming from correlated sources. Motivated by inherently high correlations between early life-cycle predictors of software reliability, we extended Murphy\u27s rule of combination to account for these correlations. When used as a part of the methodology that fuses various software reliability prediction systems, this rule provided more accurate predictions than previously reported methods. In addition, we proposed an algorithm, which defines the upper and lower bounds of the belief function of the combination results. To demonstrate its generality, we successfully applied it in the design of the Online Safety Monitor, which fuses multiple correlated time varying estimations of convergence of neural network learning in an intelligent flight control system

A Proposal on How to Use Software Reliability Growth Model to Build Confidence in Dashboard Testing

Software testing as an integral part of software development leads to the question of when the software (or application/dashboard) can be released and how confident that most defects/bugs/faults have been discovered. This paper discussed a relatively new but simple and practical proposal that can be used to build confidence for releasing software (or applications/dashboards). Instead of contrasting various software reliability growth models (SRGM) and choosing which one is the best, we use them to collaborate to help make decisions. We demonstrate our proposal with 18 real-life datasets that are publicly available in the literature. We use three widely used SRGMs, namely: Bass, Gompertz, and Logistic in our proposal to identify when we can stop testing. It turns out that when the testing has found most defects, most (if not all) of the SRGMs will converge to similar value for the maximum potential defects in the system

Reliability studies on the influence of joint clearance on the kinematics of the nose landing gear mechanism of a transport aircraft using contact theory

Contact between two objects is an important facet in multibody dynamics. It is a discontinuous, non-linear phenomenon and consequently it requires iterative simulations. The paper presents the reliability evaluation of the retraction landing gear mechanism by three contact models Viz. Impact Function Model, Coefficient of Restitution Model and Clearance Link Model. The simulations have been performed using the standard commercial multibody dynamics software ADAMS. The precision of these simulations depends on user-defined parameters like stiffness, Damping, Penetration Depth, Force exponent, Penalty and Restitution Coefficient that impacts the overall reliability of the mechanism. The optimal value of these parameters have been obtained by an optimization process using Design of Experiments tool available in ADAMS to match with the nominal values without any clearance.. The overall reliability of the mechanism has been evaluated at different instants of the retraction cycle by using Response Surface Based Monte Carlo Simulation and Direct Monte Carlo Simulation by using in house codes created in MATLAB software. The comparison, significance and accuracy of the results obtained using the above -mentioned approaches has been discussed and the impact based contact modelling for the clearance appears to be accurate and realistic for practical applications

Demonstration of a Response Time Based Remaining Useful Life (RUL) Prediction for Software Systems

Prognostic and Health Management (PHM) has been widely applied to hardware systems in the electronics and non-electronics domains but has not been explored for software. While software does not decay over time, it can degrade over release cycles. Software health management is confined to diagnostic assessments that identify problems, whereas prognostic assessment potentially indicates when in the future a problem will become detrimental. Relevant research areas such as software defect prediction, software reliability prediction, predictive maintenance of software, software degradation, and software performance prediction, exist, but all of these represent diagnostic models built upon historical data, none of which can predict an RUL for software. This paper addresses the application of PHM concepts to software systems for fault predictions and RUL estimation. Specifically, this paper addresses how PHM can be used to make decisions for software systems such as version update and upgrade, module changes, system reengineering, rejuvenation, maintenance scheduling, budgeting, and total abandonment. This paper presents a method to prognostically and continuously predict the RUL of a software system based on usage parameters (e.g., the numbers and categories of releases) and performance parameters (e.g., response time). The model developed has been validated by comparing actual data, with the results that were generated by predictive models. Statistical validation (regression validation, and k-fold cross validation) has also been carried out. A case study, based on publicly available data for the Bugzilla application is presented. This case study demonstrates that PHM concepts can be applied to software systems and RUL can be calculated to make system management decisions.Comment: This research methodology has opened up new and practical applications in the software domain. In the coming decades, we can expect a significant amount of attention and practical implementation in this area worldwid

Software reliability and dependability: a roadmap

Shifting the focus from software reliability to user-centred measures of dependability in complete software-based systems. Influencing design practice to facilitate dependability assessment. Propagating awareness of dependability issues and the use of existing, useful methods. Injecting some rigour in the use of process-related evidence for dependability assessment. Better understanding issues of diversity and variation as drivers of dependability. Bev Littlewood is founder-Director of the Centre for Software Reliability, and Professor of Software Engineering at City University, London. Prof Littlewood has worked for many years on problems associated with the modelling and evaluation of the dependability of software-based systems; he has published many papers in international journals and conference proceedings and has edited several books. Much of this work has been carried out in collaborative projects, including the successful EC-funded projects SHIP, PDCS, PDCS2, DeVa. He has been employed as a consultant t

Modeling software design diversity

Design diversity has been used for many years now as a means of achieving a degree of fault tolerance in software-based systems. Whilst there is clear evidence that the approach can be expected to deliver some increase in reliability compared with a single version, there is not agreement about the extent of this. More importantly, it remains difficult to evaluate exactly how reliable a particular diverse fault-tolerant system is. This difficulty arises because assumptions of independence of failures between different versions have been shown not to be tenable: assessment of the actual level of dependence present is therefore needed, and this is hard. In this tutorial we survey the modelling issues here, with an emphasis upon the impact these have upon the problem of assessing the reliability of fault tolerant systems. The intended audience is one of designers, assessors and project managers with only a basic knowledge of probabilities, as well as reliability experts without detailed knowledge of software, who seek an introduction to the probabilistic issues in decisions about design diversity

Design diversity: an update from research on reliability modelling

Diversity between redundant subsystems is, in various forms, a common design approach for improving system dependability. Its value in the case of software-based systems is still controversial. This paper gives an overview of reliability modelling work we carried out in recent projects on design diversity, presented in the context of previous knowledge and practice. These results provide additional insight for decisions in applying diversity and in assessing diverseredundant systems. A general observation is that, just as diversity is a very general design approach, the models of diversity can help conceptual understanding of a range of different situations. We summarise results in the general modelling of common-mode failure, in inference from observed failure data, and in decision-making for diversity in development.