Knowledge Of Application Domain Research Articles

The Relevance of Application Domain Knowledge: Characterizing the Computer Program Comprehension Process

:Recent research using professional programmers suggests that knowledge of the application domain plays a major role in the cognitive processes they use to understand computer programs. In general, programmers use a more top-down comprehension process when working in familiar application domains, and a more bottom-up process in unfamiliar domains. The present study builds on that research by further characterizing comprehension processes. The findings show that: (1) certain programmers use different types of comprehension processes depending on their familiarity with the application domain (flexible approach), while others do not (top-down and bottom-up approaches); (2) familiarity with the application domain and the use of a particular comprehension process have marked effects on references programmers make to both application and programming domain knowledge; and (3) programmers who use a flexible comprehension process achieved the highest levels of comprehension. The present research also examines some cognitive determinants of the comprehension process. The findings highlight the need to consider application, as well as programming, domain knowledge as areas of computer programming expertise, to investigate factors influencing use of specific comprehension processes, and to develop tools to support flexible comprehension processes.

USING DOMAIN KNOWLEDGE TO IMPROVE REVERSE ENGINEERING

Integrating application domain knowledge into reverse engineering is an important step to overcome the shortcomings of conventional reverse engineering approaches that are based exclusively on information derivable from source code. In this paper, we show the basic concepts of a program transformation process from a conventional to an object-oriented architecture which incorporates extraneous higher-level knowledge in its process. To which degree this knowledge might stem from some general domain knowledge, and to which extent it needs to be introduced as application dependent knowledge by a human expert is discussed. The paper discusses these issues in the context of the architectural transformation of legacy systems to an object-oriented architecture.

Integrated Systems Engineering: The Challenging Cross-Discipline

Integrated systems engineering is concerned with the systematic development of large-scale systems by means of using integrative knowledge across several disciplines. The specific knowledge of a certain application domain with its systems structures and functionalities has to be complemented with knowledge about different information technologies, human factors issues, environmental constraints and societal contexts. All these facets and components as well as their relationships to each other are briefly reviewed in this paper. Industrial systems, transportation systems, enterprise information systems, natural resources systems, and the supplementary ways for improving societal stability are the application domains mentioned here. Also, the paper refers to the different systems engineering activities of IFAC. Several systems engineering methodologies as well as integrated systems development and evaluation approaches are outlined. The paper emphasises the challenge and the need for better integrative design and development strategies for future systems. It is also an introductory paper to the broad theme of this Conference on Integrated Systems Engineering.

Research Report—The Relevance of Application Domain Knowledge: The Case of Computer Program Comprehension

The field of software, has, to date, focused almost exclusively on application-independent approaches. In this research, we demonstrate the role of application domain knowledge in the processes used to comprehend computer programs. Our research sought to reconcile two apparently conflicting theories of computer program comprehension by proposing a key role for knowledge of the application domain under examination. We argue that programmers use more top-down comprehension processes when they are familiar with the application domain. When the application domain is unfamiliar, programmers use processes that are more bottom-up in nature. We conducted a protocol analysis study of 24 professional programmers comprehending programs in familiar and unfamiliar application domains. Our findings confirm our thesis.

AUTOMATIC FUNCTIONAL MODEL GENERATION FOR PARALLEL FAULT AND DESIGN ERROR SIMULATIONS

The domain specific automatic programming technique was applied to the design and implementation of an automatic functional model generation system (AFMG) for effectively and efficiently creating simulation models of digital systems. The application domain knowledge, involved in a time-consuming and error-prone process for element modeling, was identified, organized, and incorporated into the AFMG as internal rules to guide several transformation processes for converting various forms of model specification, step by step, into desired functional models. Other design issues, such as incomplete specification handling, different signal representations, multiple-valued model generation, interaction of algorithm and data structure selection, …etc., were also carefully addressed and resolved during the design process of the AFMG system. Experimental results show that AFMG can significantly reduce the development time of creating functional models for digital logic simulation.

Acquisition and support of goal-based tasks

To make plan-based expert systems more accessible to end users and to support user tasks effectively, we present a system that makes the functionality of AI-planning techniques seem natural and immediately understandable. In particular, we present a task support system with a graphical interaction language for the acquisition and display of plan knowledge, where the intended users are domain experts and novices and where previous computer literacy is not required. Based on existing theories in cognitive science and on our own experimental research, we propose a cognitive model of the users' view of tasks. The model postulates the domain experts' ability to recall relevant parts of self-performed tasks in the application domain. The validity of the model is demonstrated in a paper-and-pencil experiment.Employing a cognitive systems engineering approach, we use the cognitive model, a stage process model of knowledge acquisition, and requirements from the plan formalism to specify DACRON, a system for plan acquisition and task support. DACRON supports the acquisition of plan knowledge by providing graphical representations of domain entities from the users' point of view. DACRON checks the consistency of specified units and graphically aids the debugging process. DACRON also allows the animated presentation of the planning process and its results. To evaluate the usability of DACRON and the relevance of the acquired and displayed knowledge in application domains, experimental studies involving 39 users were conducted. The studies show that over 90% of the subjects could easily use DACRON to enter knowledge, and 80% of the entered knowledge was relevant and correct. In the case of knowledge display, subjects were able to use the displayed knowledge effortlessly and apply it to solve 95% of the domain problems presented.

A MANAGEMENT DISCIPLINE OF SOFTWARE METRICS AND THE SOFTWARE QUALITY MANAGER

A management discipline of software metrics facilitates their consistent measurement and usage during the life cycle of software systems, by maintaining knowledge and data related to their evaluation tasks and application domain knowledge. To investigate environment of the management discipline, we represent software engineering environments by an activity flow model and a micro cycle model. A unified management view of software metrics is established by their collectivity and comprehensiveness in two-dimensional flows of the activity flow model. Then, we propose the Software Quality Manager (SQM) as a realization of the management discipline of software metrics. The proposed software quality manager makes use of two knowledge bases: (1) knowledge about application domain in the form of reusable software components and (2) knowledge about software engineering. The three-stage architecture and preparation process of the proposed system are also presented. Finally, we propose how to support the measurement and analysis task for database management systems within the context of the software quality manager.

The role of domain knowledge in software design

Abstract If effective knowledge-based support is to be provided for software designers, the process of software design, and the classes of knowledge used by designers must be understood more clearly. It has been shown that a software designer's experience of designing software in the current application domain has a significant effect on the production of a quality design. However, in gaining experience of designing software, a designer gains knowledge in various distinct areas, including software design and the application domain. It is currently unclear which elements of this experience are important. In particular, the role of application domain knowledge that is independent of software design is of great significance for builders of intelligent software design support systems, since the overheads involved in providing application domain knowledge for a variety of application domains in such systems would be huge. This paper reports on a study that has been carried out to gain insights into this questi...

Task Decomposition Through Competition in a Modular Connectionist Architecture: The What and Where Vision Tasks

A novel modular connectionist architecture is presented in which the networks composing the architecture compete to learn the training patterns. An outcome of the competition is that different networks learn different training patterns and, thus, learn to compute different functions. The architecture performs task decomposition in the sense that it learns to partition a task into two or more functionally independent tasks and allocates distinct networks to learn each task. In addition, the architecture tends to allocate to each task the network whose topology is most appropriate to that task. The architecture's performance on “what” and “where” vision tasks is presented and compared with the performance of two multilayer networks. Finally, it is noted that function decomposition is an underconstrained problem, and, thus, different modular architectures may decompose a function in different ways. A desirable decomposition can be achieved if the architecture is suitably restricted in the types of functions that it can compute. Finding appropriate restrictions is possible through the application of domain knowledge. A strength of the modular architecture is that its structure is well suited for incorporating domain knowledge.

A field study of the software design process for large systems

The problems of designing large software systems were studied through interviewing personnel from 17 large projects. A layered behavioral model is used to analyze how three of these problems—the thin spread of application domain knowledge, fluctuating and conflicting requirements, and communication bottlenecks and breakdowns—affected software productivity and quality through their impact on cognitive, social, and organizational processes.

Knowledge base refinement by monitoring abstract control knowledge

An explicit representation of the problem solving method of an expert system shell as abstract control knowledge provides a powerful foundation for learning. This paper describes the abstract control knowledge of the HERACLES expert system shell for heuristic classification problems, and describes how the ODYSSEUS apprenticeship learning program uses this representation to semi-automate “endgame” knowledge acquisition. The problem solving method of HERACLES is represented explicitly as domain-independent tasks and metarules. Metarules locate and apply domain knowledge to achieve problem solving subgoals, such as testing, refining, or differentiating between hypothesis; and asking general or clarifying questions. We show how monitoring abstract control knowledge for metarule premise failures provides a means of detecting gaps in the knowledge base. A knowledge base gap will almost always cause a metarule premise failure. We also show how abstract control knowledge plays a crucial role in using underlying domain theories for learning, especially weak domain theories. The construction of abstract control knowledge requires that the different types of knowledge that enter into problem solving be represented in different knowledge relations. This provides a foundation for the integration of underlying domain theories into a learning system, because justification of different types of new knowledge usually requires different ways of using an underlying domain theory. We advocate the construction of a definitional constraint for each knowledge relation that specifies how the relation is defined and justified in terms of underlying domain theories.

A knowledge representation language for requirements engineering

Requirements engineering, the phase of software development where the users' needs are investigated, is more and more shifting its concern from the target system towards its environment. A new generation of languages is needed to support the definition of application domain knowledge and the behavior of the universe around the computer. This paper assesses the applicability of classical knowledge representation techniques to this purpose. Requirements engineers insist, however, more on natural representation, whereas expert systems designers insist on efficient automatic use of the knowledge. Given this priority of expressiveness, two candidates emerge: the semantic networks and the techniques based on logic. They are combined in a language called the ERAE model, which is illustrated on examples, and compared to other requirements engineering languages.

Scene analysis using region-based constraint filtering

A general-purpose scene-analysis system is described which uses constraint-filtering techniques to apply domain knowledge in the interpretation of the regions extracted from a segmented image. An example is given of the configuration of the system for a particular domain, FLIR (Forward Looking InfraRed) images, as well as results of the system's performance on some typical images from this domain. A number of improvements to these techniques are proposed.

Towards An Exception Handling MechanismIn Object Oriented Design

The detection and handling of exceptions is essential to the design and implementation of reliable and reusable systems, but existing OOD methods do not provide explicit support for them at the design level. We propose an OOD model based on design for reuse with exceptions. Our approach is to categorise the possible exceptions associated with the methods of a class, with the possibility of relating then directly to formally specified pre- and postconditions. In our work, an exception is treated as an object of a general exception class which acts as a superclass for all specific exception classes. A prototype model of a tool support is proposed, which can provide advice and analysis on the application domain knowledge in designing reusable classes with exceptions.