Architecture description can be modeled as a set of alternative choices and decisions, where the rationale and tradeoffs for each decision are documented and understood as needed to inform subsequent decisions. Each decision, based on ISO/IEC/IEEE 42010, pertains to one or more stakeholder concerns. These concerns combined with the system environment and scenarios provide architecture design context that clarifies the motivation for decisions. Subsequent authors have introduced the notion of an influencing decision force, using a many-to-many relationship with concern, to provide further context for decisions. For both concerns and forces it is left to the architect to identify the nature of this context. This paper proposes a systematic process for identifying and documenting design context in support of architectural decisions. For our work decision force is used as a central unifying aspect of the architecture framework metamodel. We extend the decision Forces Viewpoint to capture detailed design context descriptions, and add features for tagging the architecture description elements to facilitate identification of commonality, classification, and specialization. Initial feedback from industry stakeholders indicates this approach should be explored further.

Nowadays large volumes of industrial data are being actively generated and collected in various power system applications. Industrial Analytics in the power system field requires more powerful and intelligent machine learning tools, strategies, and environments to properly analyze the historical data and extract predictive knowledge. This paper discusses the situation and limitations of current approaches, analytic models, and tools utilized to conduct predictive machine learning analytics for very large volumes of data where the data processing causes the processor to run out of memory. Two industrial analytics cases in the power systems field are presented. Our results indicated the feasibility of forecasting substations fault events and power load using machine learning algorithm written in MapReduce paradigm or machine learning tools specific for Big Data.

As an important element of the Smart Grid, Advanced Metering Infrastructure (AMI) systems have been implemented and deployed throughout the world in the past several years. An AMI system connects millions of end devices (e.g., smart meters and sensors in the residential level) with utility control centers via an efficient two-way communication infrastructure. AMI systems are able to exchange substantial meter data and control information between utilities and end devices in real-time or near real-time. The major challenge our research was to scale ABB's Meter Data Management System (MDMS) to manage data that originates from millions of smart meters. We designed a lightweight architecture capable of collect ever-increasing large amount of meter data from various metering systems, clean, analyze, and aggregate the meter data to support various smart grid applications. To meet critical high performance requirements, various concurrency processing techniques were implemented and integrated in our prototype. Our experiments showed that on average the implemented data file parser took about 42 minutes to complete parsing, cleaning, and aggregating 5.184 billion meter reads on a single machine with the hardware configuration of 12-core CPU, 32G RAM, and SSD Hard Drives. The throughput is about 7.38 billion meter reads (206.7GB data) per hour (i.e., 1811TB/year). In addition, well-designed publish/subscribe and communication infrastructures ensure the scalability and flexibility of the system.

Multi-core CPUs are now common in modern computers. To get access to effectively an unlimited supply of compute resources, software programs that have been highly optimized to use a single CPU need to be converted where possible to use concurrency. We have initiated our concurrency software research for performance enhancement on a large-scale system with high throughput and low latency transactions. In this paper, we report our experience, experiments, and results in various aspects of concurrency design and programming, including multi-threaded prototypes, static and dynamic concurrency analysis, future techniques and trends, concurrency experiments, and concurrency design patterns. Based on the concurrency experiments, we achieved at least 80 percent overall performance increases as measured by transaction throughput. As a result, capital expenditures for large scale deployments can be significantly reduced.

Software products are often built from commercial-off-the-shelf (COTS) components. When new releases of these components are made available for integration and testing, source code is usually not provided by the vendors. Various regression test selection techniques have been developed and have been shown to be cost effective. However, the majority of these test selection techniques rely on source code for change identification and impact analysis. In our research, we have evolved a regression test selection (RTS) process called Integrated - Black-box Approach for Component Change Identification (I-BACCI) for COTS-based applications. I-BACCI reduces the test suite based upon changes in the binary code of the COTS component using the firewall regression test selection method. In this paper, we present the Pallino tool. Pallino statically analyzes binary code to identify the code change and the impact of these changes. Based on the output of Pallino and the original test suit, testers can determine the regression test cases needed to cover the application glue code which is affected by the changed areas in the new version of the COTS component. Three case studies, examining a total of fifteen component releases, were conducted on ABB internal products. With the help of Pallino, RTS via the I-BACCI process can be completed in about one to two person hours for each release of the case studies. The total size of application and component for each release is about 340 830 KLOC. Pallino is extensible and can be modified to support other RTS methods for COTS components. Currently, Pallino works on components in Common Object File Format or Portable Executable formats

ABB incorporates a variety of commercial-off-the-shelf (COTS) components in its products. When new releases of these components are made available for integration and testing, source code is often not provided. Various regression test selection processes have been developed and have been shown to be cost effectiveness. However, the majority of these test selection techniques rely on access to source code for change identification. In this paper we present the application of the lightweight Integrated - Black-box Approach for Component Change Identification (I-BACCI) Version 3 process that select regression tests for applications that use COTS components. Two case studies, examining a total of nine new component releases, were conducted at ABB on products written in C/C++ to determine the effectiveness of I-BACCI. The results of the case studies indicate this process can reduce the required number of regression tests at least 70% without sacrificing the regression fault exposure.

No single software fault-detection technique is capable of addressing all fault-detection concerns. Similarly to software reviews and testing, static analysis tools (or automated static analysis) can be used to remove defects prior to release of a software product. To determine to what extent automated static analysis can help in the economic production of a high-quality product, we have analyzed static analysis faults and test and customer-reported failures for three large-scale industrial software systems developed at Nortel Networks. The data indicate that automated static analysis is an affordable means of software fault detection. Using the orthogonal defect classification scheme, we found that automated static analysis is effective at identifying assignment and checking faults, allowing the later software production phases to focus on more complex, functional, and algorithmic faults. A majority of the defects found by automated static analysis appear to be produced by a few key types of programmer errors and some of these types have the potential to cause security vulnerabilities. Statistical analysis results indicate the number of automated static analysis faults can be effective for identifying problem modules. Our results indicate static analysis tools are complementary to other fault-detection techniques for the economic production of a high-quality software product.

Various regression test selection techniques have been developed and have shown to improve testing cost effectiveness via improving efficiency. The majority of these test selection techniques rely on access to source code for change identification. However, when new releases of COTS components are made available for integration and testing, source code is often not available to guide in regression test selection. In this paper we describe a lightweight integrated-black-box approach for component change identification (I-BACCI) process for selection of regression tests for user/glue code that uses COTS components. I-BACCI is applicable when component licensing agreements do not preclude binary code analysis. A case study of the process was conducted on an ABB product that uses a medium-scale internal ABB software component. Six releases of the component were examined to evaluate the efficacy of the proposed process. The result of the case study indicates that this process can reduce the required regression tests by 40% on average.

Article Share on In regression testing selection when source code is not available Author: Jiang Zheng North Carolina State University, Raleigh, NC North Carolina State University, Raleigh, NCView Profile Authors Info & Claims ASE '05: Proceedings of the 20th IEEE/ACM International Conference on Automated Software EngineeringNovember 2005Pages 752–755https://doi.org/10.1145/1101908.1101997Published:07 November 2005Publication History 8citation499DownloadsMetricsTotal Citations8Total Downloads499Last 12 Months1Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

Software testing is an integral part of the software development process. Some software developers, particularly those who use the Extreme Programming test-driven development practice, continuously write automated tests to verify their code. We present a tool to complement the feedback loops created by continuous testing. The tool combines static source code metrics with dynamic test coverage for use throughout the development phase to predict a reliability estimate based on a linear combination of these values. Implemented as an open source plug-in to the Eclipse IDE, the tool facilitates the rapid transition between unit test case completions and testing feedback. The color-coded results highlight inadequate testing efforts as well as weaknesses in overall program structure. To illustrate the tool's efficacy, we share the results of its use on university software engineering course projects.