Building A Software Assessment AdvisorUsing Expert Systems Techniques

As environmental regulations become more stringent, manufacturers are confronted with the of productdesign considering environmental impacts. Life Cycle Analysis (LCA) is a developing technique that attacks this problem. LCAs provide environmental information for decision making by consumers, manufacturers, and governments. These decisions significantly affect the ability to maximize source reduction, re-use, recyclability, and recycled content. LCAs can aid in diminishing the negative impact a product has on the environment and can be utilized as a partial solution of our nations' (worlds') growing waste management problem. To date, the application of LCAs has varied from product comparison to process development and improvement. The results from these analyses have differed because of differing assumptions and differing choices of boundary conditions. An appropriate Life Cycle Analysis should be consistent across products, locations, and especially across experimenters. The development of LCAs lends itself to computer-assisted design methods such as the techniques of Expert Systems, due to the large amount of information associated with successfully developing an LCA. In this paper, we will discuss using Hierarchical Classification, a specific task specific architecture expert systems approach, to organize the terms necessary in performing an LCA. Although not yet definitive, we believe our experience indicates that such approaches to organizing the vast knowledge necessary to accomplish an LCA may be a key factor in eventual successful development of comprehensive LCAs in industry.

In this paper a model is presented for the selection of software product assessment modules, called 'Assessment bricks'. This is part of a development of an overall architecture for a tool (A Software Assessment Advisor) to act as an automated assistant to aid the assessment procedure. The model proposed consists of two sub-models: a suitability model and an applicability model. The suitability model is used to select an initial set of bricks which are suitable for the proposed assessment. The criteria used in this selection include: software characteristics to be assessed; product category; degree of relevance of bricks to identified characteristics and usability of bricks in previous assessments. The selection process involves a fuzzy algorithm and a set of rules. The applicability model provides further refinement of the selection depending upon the availability of product parts (such as documentation) , data and tools. This brick selection model is being incorporated in the Software Assessment Advisor for testing and validation. INTRODUCTION As indicated in an earlier paper[l], a Software Assessment Advisor(SAA) is a necessary tool to assist with the task of selecting a set of relevant software attributes and a suitable subset of assessment modules at the first stage of software assessment and certification. The framework of this SAA has been developed by using Expert Systems techniques. The SAA system contains a hybrid knowledge representation, a reasoning strategy and a knowledge learning Transactions on Information and Communications Technologies vol 9, © 1994 WIT Press, www.witpress.com, ISSN 1743-3517 4 Software Quality Management algorithm. The knowledge representation takes a rule-based form for the procedural knowledge which performs the attribute and assessment module (brick) selection and a frame-based form for the declarative knowledge which includes the knowledge of product aspects and the prior assessment experiences from the case studies. The reasoning strategy concerns a rule-based deduction for the basic reasoning in the selection and a frame-based closest example identification to supplement the basic reasoning. The knowledge learning algorithm involves case-based updating on the importance weights used in the rule-based selection. Further to the development of the SAA framework in [1], the first phase of implementation includes the program modules of system scheduler, input/output scheduler, reasoning scheduler, rule-based attribute selection, frame-based case identifying and a user friendly interface in Windows for man-machine communication. The implemented part is written in C and Sun View currently running under UNIX on SUN workstations. key next step in building the SAA is the development of an appropriate model for assessment module selection given that a set of relevant software attributes has been suggested for assessment and certification. About 100 assessment modules, called 'Assessment bricks', have been produced in the ESPRIT project SCOPE[2] as a modular approach for software product assessment. The approach aims to combine the use of standard assessment packages to suit the needs of any specific software product evaluation. 'Brick' encapsulates a set of measurement and assessment techniques. The basic idea in this modular approach is to consider a brick as a black-box for its selection and the evaluation of its results. This allows an effective selection and application of measurement and assessment techniques from the large set of those available. The feasibility of applying bricks has been demonstrated by a substantial number of case studies selected from specific examples of industrial software development [3] [4]. One of the main problems which has arisen concerns the selection of an appropriate set of bricks for a specific product assessment. It has been indicated by a majority of the case studies that a 'brick selection assistant' tool is necessary for an appropriate and efficient selection. model is proposed in this paper to meet the brick selection requirements; this will complete the SAA system framework. The model is constructed by using knowledge elicited from case study experiences. It consists of two submodels: a suitability model and an applicability model. The suitability model is used to select an initial set of bricks which are suitable for the proposed assessment. This selection is based on several criteria, a fuzzy algorithm and a set of selection rules. The applicability model provides further refinement of the selection depending upon the availability of product parts and tools. The following sections introduce the basic concept of assessment bricks, discuss the factors involved in the brick selection, describe the brick selection model and give an illustrative example of the selection process. Transactions on Information and Communications Technologies vol 9, © 1994 WIT Press, www.witpress.com, ISSN 1743-3517 Building Quality into Software 5 ASSESSMENT BRICKS IN THE PRODUCT CERTIFICATION PROCESS The need for a modular framework for quality assessment has been investigated in the SCOPE project. It was indicated[5]: A large majority of software assessment approaches are either oriented towards very specific products or based on the evaluation of the development process. An assessment approach aimed at all types of software product is missing, but the reason is not the lack of applicable techniques. Many quantitative techniques applicable to the various elements of a software product have been extensively experimented. However, their application is not standardised and no global framework exists. The concept of 'Assessment brick' proposed by SCOPE[6] is aimed at the development of such a modular framework for quality assessment that could combine the use of standard assessment packages to suit the needs of any specific software evaluation. The concept of 'Assessment bricks' The concept of a 'brick' is of an assessment module that encapsulates a set of measurement and assessment techniques. The module can be standardised and used to build up a software assessment plan. Essentially a 'brick' is a document that describes an assessment technique, a standard way of applying it and the result it can provide when used during the assessment task. There are two categories of 'brick': basic brick and composite brick. basic brick combines elementary metrics that can be obtained using the same technique or related techniques. composite brick combines the results of lower level 'bricks' in order to produce a more abstract result. The composite bricks are organised as a hierarchy. Product components & process evidence Estimated application cost BRICK INTERFACE DESCRIPTION BRICK APPLICATION GUIDELINES • Scope • Required input • Data to be collected • Output Estimated cost Analysis guidelines Assessment method Applicable sub-bricks • Decision guidelines • Applicable tools I Report Decision / Measure Fig. 1 The brick document framework Transactions on Information and Communications Technologies vol 9, © 1994 WIT Press, www.witpress.com, ISSN 1743-3517

Knowledge-based operation and management of communications systems

The Mission Operations Directorate (MOD) at the Johnson Space Center (JSC) is responsible for the safe operation and mission success of manned space flights. The MITRE Corporation, working with MOD, is developing requirements for the Operations Management System (OMS) to automate many aspects of flight control for the Space Station onboard systems. To help develop these requirements, the Integrated Status Assessment (ISA) expert system prototype was built to perform Station-wide failure diagnosis. This paper describes the OMS, how the ISA prototype was built, and how the prototype is being used to help define the OMS.Currently the Space Shuttle is managed on the ground using a hierarchy of control personnel. A flight director responsible for the overall Shuttle mission, a number of front room flight controllers are each responsible for a different system, and many back room controllers support various front room controllers. This system has been used since the early days of space flight and is very manpower intensive. A similar hierarchy is seen in an automated system for the Space Station. The back room controllers can be augmented with smart components and sensors on the Station and the front room controllers can have their jobs aided with expert systems. At the highest level is the OMS which works at the flight director level getting summary information from the various systems in the Station.The OMS will include onboard automation, ground based automation, onboard manual operations, and ground based operations. One of the functions of the OMS is to perform a Station-wide status assessment and failure diagnosis of the Station. This Station-wide view is very important because all of the systems interact with each other and a failure in one system will have impacts on the other systems.It became evident early in discussion with to the MOD flight controllers that prototypes would be very helpful in the process of gathering requirements for the OMS. The prototypes were used to gather additional requirements, to expose people to new ideas and technologies, and study the feasibility of these technologies for systems management.Because the task of assessing the status of space vehicles is a complex job that requires “expert” knowledge to find heuristic solutions to problems, an expert system approach was chosen to prototype the ISA system. The initial domain of the expert system was the KU band portion of the communications and tracking system (one of the onboard “core” Space Station Systems).The ISA prototype expert system consists of a knowledge base, an inference engine, and a user interface. The ISA system was designed as a hybrid expert system using different methodologies: object-oriented programming, rule-based programming using both shallow and deep reasoning, and qualitative modeling.The ISA prototype has been very successful in its goals. ISA has demonstrated new technology and ideas to the NASA operations community, helped define requirements for the OMS, and shown the feasibility of using hierarchical expert systems to monitor complex systems. The ISA prototype, along with other OMS prototypes, are being integrated in a test bed environment with other space station prototypes to form the Space Station Information System (SSIS) End-to-end Test Capability (ETC) which will have components all over the world. The results of the ETC will influence the Space Station design and be a test environment for software and hardware for years to come.

Expert-system technology has emerged from beneath the umbrella of Artificial Intelligence and into the mainstream of engineering problem solving. There already are several notable examples of the application of this state-of-the-art technology in the petroleum industry. Clearly, expert systems that run efficiently on standard personal computers are particularly of value. A new expert-system generator that overcomes the speed, memory and mass-storage limitations of XT/AT-class personal computers is presented in this paper. The shell is optimized for the specific characteristics of the target user, knowledge expert and computer. Expert systems developed using this shell currently are being used by our technical personnel for drilling fluid and related applications.

8 - Real-time fault-tolerant control systems

Decreasing supporting resources and increasing complexity of horticultural crop production necessitate the new technologies be developed to transfer information to farmers as well as commercial producers. Expert systems, a sub area of artificial intelligence of computer science offer the possibility to implement horticultural knowledge into field practice in the form of computer programme in order to support the farmers in decision making at farm level. This paper provides an overview of the concept of expert system and examples of areas of horticultural applications of these systems are in use at present are also outlined. This paper further examines how expert system techniques are being used at present in problem solving and why someone in the field of horticulture might want to use them.

Diagnosing the vibrating behaviour of the PWR internal structures using expert systems techniques and algorithmic treatment

Expert system aid for design of overload line distribution networks

With the Rock Engineering Systems (RES) methodology, rock mechanics and rock engineering problems are studied systematically using a total systems approach, incorporating rock mass properties, interrelated parameters, complex interaction mechanisms and dynamic behavioral modes. In this paper, a method of implementation and computerization of RES is considered, using neural networks together with an expert system. The computerized RES starts with the data processing of rock mass properties and boundary conditions and data base management of rock engineering case records. This step is followed by building and operating parameter interaction matrices with the combined use of backpropagation networks and an expert system. Finally, a simulator for modelling the dynamic process of rock engineering systems using the Hopfield network is incorporated. With the aid of neural networks' learning capability and expert system's symbol-reasoning capability, the RES approach is implemented in an “intelligent” mode.

Mineral identification using x-ray powder diffractometry (XRD) requires human judgment and heuristics. Thus, the task is admirably suited for an expert system approach. Expert systems are computer programs which emulate human expertise. The power of an expert system is derived from the knowledge the system embodies, rather than from search algorithms. An expert system helps solve problems for which well-defined algorithmic solutions are difficult to obtain. The authors have coded an expert system, XRAYS, to identify minerals via x-ray diffractograms. The system emulates the well-known manual Hanawalt method, thus avoiding the black-box approach of some computer search/match programs. The mineral subfile of the JCPDS file is stored in a database file, from which the Hanawalt groups are created. The expert system then carries out a manual search following exactly the steps prescribed for the Hanawalt method. In the program, both peak positions and intensities are represented by fuzzy numbers. Fuzzy comparisons and fuzzy arithmetic operations are employed in searching for matches. A list of candidate minerals is output in decreasing order of confidence. Other information, such as chemistry, rock type, suspected minerals, etc. can be coded as production rules, thereby further narrowing the list of candidate minerals. Examples composed ofmore » typical mineral suites in sedimentary rocks will be given.« less

An expert system for judicious selection of a robot for an industrial task has been developed. The expert system has been realised to meet the user’s requirement. However further development is needed to satisfy the requirements of multiple constraints.

Intelligent process-control system design

Implementation of artificial intelligence methods in NDT: expert system approach

The aim of this paper is to present a Fuzzy Expert System Methodology (FESM), based on the Fuzzy Logic, Expert System techniques and multicriteria decision making theory, which can be used to calculate a unique Fuzzy Total Power Quality Index (FTPQI) for every measured point of the electric network (measurements for a specific time period are used). FTPQI of the specific point includes unified and concentrated information about its PQ level and could contribute to the evaluation of examined electric network nodes providing a ranking between them in terms of PQ using the corresponding FTPQI. This tool could be used by the system administrator giving a reliable and scientific way to check the total PQ of electric networks in general and at specified points and, also, providing the possibility of ad-hoc network improvement remedies. (6 pages)