Shared software in the production environment

Smart Materials: Integrated Design, Engineering Approaches, and Potential Applications

Chapter 1 - Basics and Roots of Synthetic Biology

The paper focuses on development and application of a software engineering system BPsim.SD, part of decision support suite BPsim.DSS, which greatly simplifies analysts work and their interaction with software developers. System is integrated into a decision support suite, which allows instant transition from software entities design to simulation of developed product with possible application of simulation experiment design for tough projects. Paper gives several examples of the system deployment in production environments, both for model validation (which was successful) and for software design from scratch, which resulted in benefits from decreased costs and avoided data losses. After verification the system has been used in multiple projects. This paper describes software application for design of an information system for shareholders registers, located in Russia.

DevOps [1] is a recent trend in software engineering that bridges the gap between software development and operations, putting the developer in greater control of the operational environment in which the application runs. To support Quality-of-Service (QoS) analysis, the developer may rely on software performance models. However, to provide reliable estimates, the input parameters must be continuously updated and accurately estimated. Accurate estimation is challenging because some parameters are not explicitly tracked by log files requiring deep monitoring instrumentation that poses large overheads, unacceptable in production environments.

In this chapter, we present knowledge engineering (KE) and knowledge management (KM) techniques and applications in design. Then, we introduce an original model of knowledge representation called Hypertopic, and different methods of building collective knowledge representations based on Hypertopic.

Requirement analysis and approach for Chaos Engineering in industrial production

For 15 years, the Software Engineering Laboratory (SEL) has been carrying out studies and experiments for the purpose of understanding, assessing, and improving software and software processes within a production software development environment at NASA/GSFC. The SEL comprises three major organizations: (1) NASA/GSFC, Flight Dynamics Division; (2) University of Maryland, Department of Computer Science; and (3) Computer Sciences Corporation, Flight Dynamics Technology Group. These organizations have jointly carried out several hundred software studies, producing hundreds of reports, papers, and documents, all of which describe some aspect of the software engineering technology that was analyzed in the flight dynamics environment at NASA. The studies range from small, controlled experiments (such as analyzing the effectiveness of code reading versus that of functional testing) to large, multiple project studies (such as assessing the impacts of Ada on a production environment). The organization's driving goal is to improve the software process continually, so that sustained improvement may be observed in the resulting products. This paper discusses the SEL as a functioning example of an operational software experience factory and summarizes the characteristics of and major lessons learned from 15 years of SEL operations.

Malware is a man-made evil code, created for manipulative and destructive purpose. The increasing dependence on today's Internet and other communication network has caused a major malware threat to many computer users. The threat can infiltrate computers using a variety of methods, such as hidden functionality in regular programs, drive-by download from unsafe web sites, attack against known software vulnerabilities and more. In this paper, architecture of modern malware scanning engine is proposed and presented. A known packer detector and removal is proposed to build on top of the core engine. Prior begin malware scanning engine, the detection of known packer has to be performed. If any known packer is detected, a dedicated decryption routine will strip out the packer protection. Our malware detection core engine approach is based on the integration of static heuristic, emulator and disassembler. Static heuristic scanner detects malicious program via byte signature identification. It involves static extraction of an executable file and compares the destructive code with dedicated viral signatures. Emulator can execute the arbitrary code of an instance and trace the instance body's code inside the virtual environment. It can be used to combat any protection code, regardless of the complexity of the protection algorithm. Disassembler module will work simultaneously with emulator to analyze the execution code. Fragment of malicious code within the decrypted virus body could be detected via the execution. Through this study, we hope to help security researchers to understand our defense approach and give some directions for future research.

An integrated avionic integration and support facility for advanced aircraft is described, as are the engineering systems used in the development and integration process. Several types of stations are used for various development stages. The functions of the stations vary, but common architecture, hardware subsystems and software packages are used in stations implementation. This approach provides substantial benefits in facility design, usage and maintenance. The main and most complex station is the Dynamic Test Station (DTS). The DTS functions, architecture and software are described in detail. One of the features of the system is the usage of some actual aircraft computers with their operational software, while maintaining the capability to control the environment of the unit under test. >

Cloud computing is an emerging commercial infrastructure paradigm that promises to eliminate the need for maintaining expensive computing facilities by companies and institutes alike. Through the use of virtualization and resource time sharing, clouds serve with a single set of physical resources a large user base with different needs. Thus, clouds have the potential to provide to their owners the benefits of an economy of scale and, at the same time, become an alternative for scientists to clusters, grids, and parallel production environments. However, the current commercial clouds have been built to support web and small database workloads, which are very different from typical scientific computing workloads. Moreover, the use of virtualization and resource time sharing may introduce significant performance penalties for the demanding scientific computing workloads. In this work, we analyze the performance of cloud computing services for scientific computing workloads. We quantify the presence in real scientific computing workloads of Many-Task Computing (MTC) users, that is, of users who employ loosely coupled applications comprising many tasks to achieve their scientific goals. Then, we perform an empirical evaluation of the performance of four commercial cloud computing services including Amazon EC2, which is currently the largest commercial cloud. Last, we compare through trace-based simulation the performance characteristics and cost models of clouds and other scientific computing platforms, for general and MTC-based scientific computing workloads. Our results indicate that the current clouds need an order of magnitude in performance improvement to be useful to the scientific community, and show which improvements should be considered first to address this discrepancy between offer and demand.

Recent years have seen important advances in computational linguistics and artificial intelligence. Although many problems remain, the goal of providing limited English-processing facilities for non-technical computer users is within sight. By the end of the decade, numerous systems providing limited coverage of "natural language" will be available for business and home use. Several systems (e.g. TQA [16]) have already become operational. One system (ROBOT [7]) has been supporting natural language inputs in a dozen or so different commercial database applications for at least three years. Many other systems have been developed to the prototype stage and will soon be able to be transferred, with varying degrees of effort, from a research to a production environment. Each system tends to provide special features of its own, and the future prospects for database, office, instructional, and other environments are quite exciting.