Developing Reliable yet Flexible Software through If-Then Model Transformation Rules

Development of next generation of smarter machines and services requires building an easy-to-use technology stack. These technologies include hardware, embedded software, data, and applications. Smarter machines support carrying out the precise job, better decision making, and create new ways of doing things to reduce cost and increase speed, accuracy, and automation. As technology meets iron through smart machines, there is increased embedded software within products. To provide distinctive customer experience for a solution system, along with reliability of hardware, development of reliable software also plays a critical role. This paper proposes models in which software reliability is a function of the number of residual faults and is measured with the help of software metrics based on development data. The intent is to establish a statistical relationship between product metrics (that deal with the measurement of the software product) or process metrics (the process by which it is developed) with measures of quality. Using both a pattern recognition algorithm approach for classifying fault proneness and applying fuzzy logic to software metrics for defect prediction is found to be beneficial for improving software reliability prediction in early development phases. This paper proposes non-parametric models like Artificial Neural Network from deep learning to predict expected number of failures utilizing past failure data for software reliability estimation and release readiness during launch.

Software reliability engineering is not only the use of software reliability models and similar techniques, it is the use of sensible engineering principles with cost/benefit analysis throughout the software life cycle to obtain reliable software. The need to have a comprehensive view on software development to engineer reliable software is emphasized. Cleanroom Software Engineering is proposed as being the basis for developing reliable software. The paper in particular discusses some extensions to Cleanroom, both in terms of adaptations and additions. Particular emphasis is on high-level design techniques and methods for reliability certification of the software. The comprehensive view of software is supported by several success stores, both with references to results presented in literature as well as experiences from projects conducted by Q-Labs. The results obtained are encouraging. The methods proposed are shown to give a substantial gain in the development of reliable software.

The monograph provides a detailed presentation of the main approaches to the creation of formalized descriptions of various objects, processes, and systems based on visual modeling tools. The evolution of visual models based on graph representations is examined. A classification of visual information modeling tools is presented. Information is provided on the principles of applying domain modeling methods based on formal visual modeling languages. The monograph may be useful to a wide range of readers, including experienced specialists in the field of information modeling of various processes, systems, and technologies.

An analysis of factors affecting software reliability

Size, complexity, and human dependency on software--based products have grown dramatically during past decades. Software developers are struggling to deliver reliable software with acceptable level of quality, within given budget and schedule. One measure of software quality and reliability is the number of residual faults. Therefore, researchers are focusing on the identification of the number of fault presents in the software or identification of program modules that are most likely to contain faults. A lot of models have been developed using various techniques. A common approach is followed for software reliability prediction utilizing failure data. Software reliability and quality prediction is highly desired by the stakeholders, developers, managers, and end users. Detecting software faults early during development will definitely improve the reliability and quality in cost-effective way.KeywordsSoftware ReliabilityFault PredictionSoftware MetricsCapability Maturity ModelTest Case PrioritizationThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

The purpose of the study is to substantiate the feasibility of using the method of visual modeling based on mnemonic tables as a means of forming monological speech in the native Russian language of older preschool children. The article examines the features of speech development of children aged 5-7, discusses the possibility of using visual modeling as a means of improving the effectiveness of speech development of older preschoolers, and also defines pedagogical conditions that contribute to improving the effectiveness of the monologue speech learning process for children aged 5-7. The effectiveness of using the visual modeling method as a means of developing monological speech in older preschoolers is noted. The scientific novelty of the study lies in determining the pedagogical conditions for the development of monological speech of older preschool children through visual modeling (availability of visibility; observance of the principle of transition from simple to complex; provision of visual models of the developing subject-spatial environment of a preschool educational organization). As a result, the positive effect of using mnemonic tables in the process of teaching monologue speech to older preschool children has been proven.

Cyber Physical Systems CPS bridge the cyber-world of computing and communications with the physical world and require development of secure and reliable software. It asserts a big challenge not only on testing and verifying the correctness of all physical and cyber components of such big systems, but also on integration of these components. This paper develops a categorization of multiple levels of testing required to test CPS and makes a comparison of these levels with the levels of software testing based on the V-model. It presents a detailed state-of-the-art survey on the testing approaches performed on the CPS. Further, it provides challenges in CPS testing.

With the rapid development of science and information technology, software systems are widely used in all walks of life in society. Composite materials have become the hotspot and trend in pipe development, which greatly broaden the application field of composite pipes. Aiming at the requirement of composite pipe section design in composite flexible pipe factories in actual production, this paper simplifies the flexible composite pipe into a uniform continuous model, and develops a pipe section design software based on Visual Basic environment. According to the numerical calculation method in mechanics and the empirical formula, considering the production error and safety factor, this paper puts forward a reasonable method for designing the flexible composite pipe section, and develops a system for calculating it using Visual Basic. Based on the performance of raw materials and the requirement of customers for pipe pressure, this software can figure out the section data of steel strip pipe, steel cord pipe, polyester pipe, Kevlar pipe, and fiberglass pipe respectively, and obtain the design pressure, minimum bending radius and other parameters, thus improving the efficiency of the design process. At the same time, raw material quotation and pipe cost price can be calculated to facilitate the purchase of raw materials, pipe storage and sales. This software is an ideal tool for calculating flexible composite pipe cross-section data.

Despite the successful contributions in the field of network intrusion detection using machine learning algorithms and deep networks to learn the boundaries between normal traffic and network attacks, it is still challenging to detect various attacks with high performance. In this paper, we propose a novel mathematical model for further development of robust, reliable, and efficient software for practical intrusion detection applications. In this present work, we are concerned with optimal hyperparameters tuned for high performance sparse autoencoders for optimizing features and classifying normal and abnormal traffic patterns. The proposed framework allows the parameters of the back-propagation learning algorithm to be tuned with respect to the performance and architecture of the sparse autoencoder through a sequence of trigonometric simplex designs. These hyperparameters include the number of nodes in the hidden layer, learning rate of the hidden layer, and learning rate of the output layer. It is expected to achieve better results in extracting features and adapting to various levels of learning hierarchy as different layers of the autoencoder are characterized by different learning rates in the proposed framework. The idea is viewed such that every learning rate of a hidden layer is a dimension in a multidimensional space. Hence, a vector of the adaptive learning rates is implemented for the multiple layers of the network to accelerate the processing time that is required for the network to learn the mapping towards a combination of enhanced features and the optimal synaptic weights in the multiple layers for a given problem. The suggested framework is tested on CICIDS2017, a reliable intrusion detection dataset that covers all the common, updated intrusions and cyber-attacks. Experimental results demonstrate that the proposed architecture for intrusion detection yields superior performance compared to recently published algorithms in terms of classification accuracy and F-measure results.

In a smart energy system, power converters should be controlled by complex control software. However the efficient development of reliable control software is difficult. We have proposed to introduce a “model-based design” process using MATLAB/Simulink, established in the automotive and aerospace fields, to the development of power electronics systems. However, it was difficult in the past because calculating switching circuit as the plant model is difficult in MATLAB/Simulink due to the socalled stiff equation problem. Thus, in this study, we have developed a simulation environment in which the standard model-based design tool, MATLAB/Simulink, and the simulation engine for power switching circuits, SCALE, work cooperatively. SCALE was originally developed to solve stiff equation and efficiently simulate power switching circuits. This software environment enables us to design control algorithms with intuitive block diagram expression and test them in actual hardware, without writing a C program. Furthermore to improve this environment, a mechanism that handles the multiple sample rates was introduced. Smart energy systems that use several controllers with different sampling rates can be simulated in the proposed environment. Thus, we can develop control software for smart energy systems in a timely and cost effective manner.

Putting Requirements and Quality at the Core of Global Service Delivery: Current Efforts and Future Plans at Pace University

One of the effective methodological approaches in programming that supports the design and development of reliable software is analogy-based programming. Within this framework, the method of problem reduction plays a key role. Reducing a given problem to another one whose solving algorithm is already known can be made more efficient by the application of programming theorems. These represent proven, abstract solutions – in a general form – to some of the most common problems in programming. In this article, we present six fundamental programming theorems as well as pose five sample problems. In solving these problems, all six programming theorems will be applied. In the process of reduction, we will employ a concise specification language. Programming theorems and solutions to the problems will be given using the structogram form. However, we will use pseudocodes as descriptions of algorithms resembling their actual implementation in Python. A functional style solution to one of the problems will also be presented, which is to illustrate that for the implementation in Python, it is sufficient to give the specification of the problem for the design of the solution. The content of the article essentially corresponds to that of the introductory lectures of a course we offered to students enrolled in the Applied Mathematics specialization. Subject Classification: D40

Implementing specification freedoms

Verification of HOS software specification by a hierarchical software understanding approach

We present business archetypes and archetype patterns based methodology for modelling of business domains. Business domain model (e.g. in health care, banking, transportation, etc.) describes universe of discourse of business without any reference to the software requirements or to the design to any software system. Business archetype is an abstract model describing some common term that consistently and universally occurs in business domains and in business software systems. Business archetype pattern is collaboration between business archetypes. Product, process, party, party relationship, inventory, order, quantity and rule archetype patterns are members of this archetype patterns framework. We exemplify the usefulness of this framework of business archetypes and archetype patterns by utilizing it in development of clinical laboratory domain model and of clinical laboratory information management system (LIMS) software based on this domain model. In software development we follow the software engineering triptych - from domain modelling via requirements constructing to design and implementation of software. In our understanding the domain modelling with archetypes and archetype patterns complements Bjørner's domain facets based domain analysis methodology and results in more flexible, customizable, reliable and interoperable software.