J. Christian Wild

Enterprise Modeling and Decision-Support for Automating the Business Rules Lifecycle

Business rules represent policies, procedures and constraints regarding how an enterprise conducts its business. To get the full benefits of modeling business rules requires an approach to managing them through their full lifecycle, from acquisition through deployment and evolution. The research reported in this paper is aimed at determining what infrastructure capabilities are needed to provide this lifecycle support. The solution embodies a modeling framework that captures the structure of the enterprise, in terms of which the business rules can be expressed, and decision-support capabilities for reasoning about and deriving business rules. The paper demonstrates the possibility of automatic support of the business rules lifecycle by automatically generating business rules from the captured information, along with data representing domain assumptions in a case study (the London Ambulance System). A system was implemented to illustrate the methodology and to demonstrate the feasibility of the approach. The methodology also gives guidance on how to deal with pragmatically important situations such as rules that involve both automated and human tasks, nondeterministic rules, and goal-oriented versus operational rules.

Application of a decision support mechanism to the business rules lifecycle

Decision structures have been proposed in a number of contexts an knowledge-based software engineering as an important mechanism for recording and reasoning about the information needed to make decisions during the software lifecycle. We apply decision structures to a new domain, namely that of the business rules of an enterprise. Given the dynamic nature of business rules, decision structures are seen to be an appropriate framework to record and evolve business rules. The paper describes how to combine decision structures and business rules within a conceptual modeling framework. An architecture is presented that addresses business rules throughout the operational lifetime of the systems they govern.

A knowledge base for software test refinement

Software testing criteria produce test descriptions that may be viewed as systems of constraints describing desired test cases. Refinement of test descriptions is possible by adding additional constraints to each test description, reducing the solution space and focusing attention upon tests that are more likely to reveal faults. This paper describes the structure of a knowledge base intended to capture potentially useful refinements, based either upon the expert knowledge of a tester or upon the software faults uncovered in prior, related projects.<<ETX>>

Employing accumulated knowledge to refine test descriptions

Most testing methods generate test descriptions which define the desired characteristics of the input data in a test case. This paper describes the use of accumulated knowledge about a problem domain to refine these test descriptions, with the goal of increasing the probability that the input data generated from the refined test descriptions will reveal faults in a software system. A knowledge base is introduced to hold information about object semantics and object class/subclass relationships. Knowledge accumulates with experience in a particular domain and can be focused on those objects and relationships in that domain which experience has shown to be error‐prone. This paper also defines a knowledge‐driven functional testing (KDFT) method which derives test descriptions from a formal specification and refines these descriptions using that knowledge base. A case study of the KDFT method using data from a previous study of the launch intercept control problem is described. These preliminary results indicate that knowledge‐based refinement of test descriptions can dramatically improve their ability to detect certain classes of faults.

Computer Productivity Initiative

Over the last three decades computer science has evolved into a mature and experimentally oriented discipline with a well defined curriculum. Only recently have we come to realize that as a discipline computer science must reach beyond its own subject area to applications in other disciplines in order to stay relevant. Most computer science curricula teach principles and programming skills in isolation from an application perspective, provide limited laboratory experience, and introduce inadequate integration with non-CS components. The Computer Productivity Initiative, described in this paper, proposes to alleviate these problems by integrating a multi-year project into the curriculum. The project involves courses normally taken in three different years of the curriculum. It includes hardware and software issues and also addresses engineering, business, and other non-CS issues. The initiative uses prototyping and simulations in the development of specifications for an integrated television communication and display computer system. The students apply principles of productivity and make extensive use of leading-edge technologies both in the process of the project development and the product being developed. They hone essential career-oriented skills in the areas of management, formal presentations, and group problem solving. This paper is a report of work in progress. It emphasizes the implementation issues we are facing and the integration of evaluation into our curriculum development. It describes our preparation for the dissemination of a model curriculum when we are able to demonstrate that the approach is adaptable to CS departments across the country.

Computer Productivity Initiative: Past, Present, Future

The Computer Productivity Initiative (CPI) is a jointly funded effort by Old Dominion University and the National Science Foundation to address some shortcomings of the traditional CS curriculum. In CPI students apply CS knowledge in the context of a broad range of issues affecting the productive employment of CS technology. The CPI program is also directed towards the development of career skills including group interaction, technical communications, and interviewing as well as domain analysis. This paper discusses the lessons learned from the two year effort to implement this new program. These include: using an external board of industry executives for the final review of senior-level course projects is highly motivating and effective; students take longer than expected to gain competence but then become more competent than expected; a relatively small class size is necessary to implement a “learn by doing” approach; building a prototype to demonstrate concept and assess risks is very effective but can be time consuming; evaluation of a proposed implementation is difficult in the absence of a “real” customer; a better method of reality checks is needed; tight schedules are difficult to fit into traditional semester boundaries; students are enthusiastic about the program and gain confidence in their ability to enter their careers; the level of effort is comparable to that in project oriented courses for both students and instructors; CPI graduates report increased responsibilities and pay as compared to their counterparts.