Robust Dynamic Selection of Tested Modules in Software Testing for Maximizing Delivered Reliability

Software testing is aimed to improve the delivered reliability of the users. Delivered reliability is the reliability of using the software after it is delivered to the users. Usually the software consists of many modules. Thus, the delivered reliability is dependent on the operational profile which specifies how the users will use these modules as well as the defect number remaining in each module. Therefore, a good testing policy should take the operational profile into account and dynamically select tested modules according to the current state of the software during the testing process. This paper discusses how to dynamically select tested modules in order to maximize delivered reliability by formulating the selection problem as a dynamic programming problem. As the testing process is performed only once, risk must be considered during the testing process, which is described by the tester's utility function in this paper. Besides, since usually the tester has no accurate estimate of the operational profile, by employing robust optimization technique, we analysis the selection problem in the worst case, given the uncertainty set of operational profile. By numerical examples, we show the necessity of maximizing delivered reliability directly and using robust optimization technique when the tester has no clear idea of the operational profile. Moreover, it is shown that the risk averse behavior of the tester has a major influence on the delivered reliability.Comment: 19 pages, 4 figure

Decision support for build-to-order supply chain management through multiobjective optimization

This paper aims to identify the gaps in decision-making support based on multiobjective optimization for build-to-order supply chain management (BTOSCM). To this end, it reviews the literature available on modelling build-to-order supply chains (BTO-SC) with the focus on adopting multiobjective optimization (MOO) techniques as a decision support tool. The literature has been classified based on the nature of the decisions in different part of the supply chain, and the key decision areas across a typical BTO-SC are discussed in detail. Available software packages suitable for supporting decision making in BTO supply chains are also identified and their related solutions are outlined. The gap between the modelling and optimization techniques developed in the literature and the decision support needed in practice are highlighted and future research directions to better exploit the decision support capabilities of MOO are proposed

Decision support for build-to-order supply chain management through multiobjective optimization

This is the post-print version of the final paper published in International Journal of Production Economics. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2010 Elsevier B.V.This paper aims to identify the gaps in decision-making support based on multiobjective optimization (MOO) for build-to-order supply chain management (BTO-SCM). To this end, it reviews the literature available on modelling build-to-order supply chains (BTO-SC) with the focus on adopting MOO techniques as a decision support tool. The literature has been classified based on the nature of the decisions in different part of the supply chain, and the key decision areas across a typical BTO-SC are discussed in detail. Available software packages suitable for supporting decision making in BTO supply chains are also identified and their related solutions are outlined. The gap between the modelling and optimization techniques developed in the literature and the decision support needed in practice are highlighted. Future research directions to better exploit the decision support capabilities of MOO are proposed. These include: reformulation of the extant optimization models with a MOO perspective, development of decision supports for interfaces not involving manufacturers, development of scenarios around service-based objectives, development of efficient solution tools, considering the interests of each supply chain party as a separate objective to account for fair treatment of their requirements, and applying the existing methodologies on real-life data sets.Brunel Research Initiative and Enterprise Fund (BRIEF

Subscale Mars Colonization Mission

The team will compete in the SAE (Society of Automotive Engineers) Aero Design West Advanced Class competition, held 5-7th April 2019 in Van Nuys, California. The team will work to develop, through research, design, optimizational trade studies, and manufacturing, a system for the deployment of parasitic aircraft, as well as payload. The system will consist of a primary fixed-wing aircraft, parasitic autonomous gliders, a real-time altitude data acquisition system, as well as both static and releasable payload. The deployable gliders must navigate autonomously to a targeting area on the ground without any on-board propulsion. The releasable payload, which will consist of habitat modules and water bottles, must also be dropped in such a manner that they land within the same targeting area. The primary aircraft must be powered through only electric power, with an installed power limiter of 750 watts. In general, the wing and powertrain must be optimized to provide sufficient lift given a limit on available power. The project will require robust systems-level engineering as well as rigorous research, design, testing, and iteration

X-33 Flight Operations Center

In response to Clause 17 of the Cooperative Agreement NCC8-115, Lockheed Martin Skunk Works has compiled an Annual Performance Report of the X-33/RLV Program. This report consists of individual reports from all industry team members, as well as NASA team centers. Contract award was announced on July 2, 1996 and the first milestone was hand delivered to NASA MSFC on July 17, 1996. With the dedication of the launch site, and continuing excellence in technological achievement, the third year of the Cooperative Agreement has been one of outstanding accomplishment and excitement

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

Application of Modified Agile Methodology to Improve Usability of SAP ECC

Enterprise Resource Planning application has become the basic need for business in recent times. Most organizations have implemented ERP application despite the configuration and cost issues, but are not exploiting the true benefits of application. In this project the usability of SAP R/3 application (which is a leading ERP vendor in the market) was studied and how SAP Fiori implementation can overcome few of the drawbacks in application usability is explained. Implementation cost, Application Configuration, difficulty in use and poor training to employees are the major drawbacks of SAP R/3 application. SAP Fiori is a set of applications developed by SAP, which target the most frequently-used SAP transaction codes, workflow approvals and various other self-service reporting needs. SAP Fiori provides simple and friendly user experience across multiple devices like smartphones, tablets, and desktops. Project management in Implementation of customized SAP Fiori app for HCM module is discussed and the impact of new app on the organization is also analyzed as part of this project. Finally, feedback of the end user is collected to understand the usability of the new SAP Fiori HCM application

Exploring formal verification methodology for FPGA-based digital systems.

Abstract Not Provide

The "MIND" Scalable PIM Architecture

MIND (Memory, Intelligence, and Network Device) is an advanced parallel computer architecture for high performance computing and scalable embedded processing. It is a Processor-in-Memory (PIM) architecture integrating both DRAM bit cells and CMOS logic devices on the same silicon die. MIND is multicore with multiple memory/processor nodes on each chip and supports global shared memory across systems of MIND components. MIND is distinguished from other PIM architectures in that it incorporates mechanisms for efficient support of a global parallel execution model based on the semantics of message-driven multithreaded split-transaction processing. MIND is designed to operate either in conjunction with other conventional microprocessors or in standalone arrays of like devices. It also incorporates mechanisms for fault tolerance, real time execution, and active power management. This paper describes the major elements and operational methods of the MIND architecture