Marek Reformat

Software cost estimation with fuzzy models

Estimation of effort/cost required for development of software products is inherently associated with uncertainty. In this paper, we are concerned with a fuzzy set-based generalization of the COCOMO model (f-COCOMO). The inputs of the standard COCOMO model include an estimation of project size and an evaluation of other parameters. Rather than using a single number, the software size can be regarded as a fuzzy set (fuzzy number) yielding the cost estimate also in form of a fuzzy set. The paper includes detailed results with this regard by relating fuzzy sets of project size with the fuzzy set of effort. The analysis is carried out for several commonly encountered classes of membership functions (such as triangular and parabolic fuzzy sets). The issue of designer-friendliness of the f-COCOMO model is discussed in detail. Here we emphasize a way of propagation of uncertainty and ensuing visualization of the resulting effort (cost). Furthermore we augment the model by admitting software systems to belong partially to the three main categories (namely embedded, semidetached and organic) and discuss key implications of this generalization and highlight its links with a generalized sensitivity analysis. The experimental part of the study illustrates the approach and contrasts it with the standard numeric version of the COCOMO model.

Evolutionary fuzzy modeling

This study is concerned with a general methodology of identification of fuzzy models. Unlike numeric models, fuzzy models operate at a level of information granules - fuzzy sets - and this aspect brings up an important design requirement of transparency of the model. We propose a three-phase development framework by distinguishing between structural and parametric optimization processes. The underlying topology of the model dwells on fuzzy neural networks - architectures governed by fuzzy logic and equipped with parametric flexibility. Two general optimization mechanisms are explored: the structural optimization is realized via genetic programming whereas for the ensuing detailed parametric optimization we proceed with gradient-based learning. The main advantages of this approach are discussed in detail. The study is illustrated with the aid of a numeric example that provides a detailed insight into the performance of the fuzzy models and quantifies crucial design issues.

Automatic test data generation using genetic algorithm and program dependence graphs

Abstract The complexity of software systems has been increasing dramatically in the past decade, and software testing as a labor-intensive component is becoming more and more expensive. Testing costs often account for up to 50% of the total expense of software development; hence any techniques leading to the automatic generation of test data will have great potential to considerably reduce costs. Existing approaches of automatic test data generation have achieved some success by using evolutionary computation algorithms, but they are unable to deal with Boolean variables or enumerated types and they need to be improved in many other aspects. This paper presents a new approach utilizing program dependence analysis techniques and genetic algorithms (GAs) to generate test data. A set of experiments using the new approach is reported to show its effectiveness and efficiency based upon established criterion.

Ontological approach to development of computing with words based systems

Computing with words introduced by Zadeh becomes a very important concept in processing of knowledge represented in the form of propositions. Two aspects of this concept - approximation and personalization - are essential to the process of building intelligent systems for human-centric computing. For the last several years, Artificial Intelligence community has used ontology as a means for representing knowledge. Recently, the development of a new Internet paradigm - the Semantic Web - has led to introduction of another form of ontology. It allows for defining concepts, identifying relationships among these concepts, and representing concrete information. In other words, an ontology has become a very powerful way of representing not only information but also its semantics. The paper proposes an application of ontology, in the sense of the Semantic Web, for development of computing with words based systems capable of performing operations on propositions including their semantics. The ontology-based approach is very flexible and provides a rich environment for expressing different types of information including perceptions. It also provides a simple way of personalization of propositions. An architecture of computing with words based system is proposed. A prototype of such a system is described.

Local descriptors in application to the aging problem in face recognition

Local descriptors are widely used in face recognition due to their robustness to changes in expression or occlusion in facial images. In this paper, a comparison of local descriptors commonly used in face recognition methods is presented in the context of age changes of individuals. We quantify abilities of local descriptors used in face recognition in the context of age discrimination. The performance of the descriptors is evaluated by experimenting with the FG-NET database. We present the results for different age groups and for various age differences of individuals present in the training and testing images. The values of recognition accuracy are reported in combination with various similarity measures used for classification purposes. Moreover, the performance of the descriptors combined with Gabor wavelet images is tested.

Empirical evaluation of optimization algorithms when used in goal-oriented automated test data generation techniques

Software testing is an essential process in software development. Software testing is very costly, often consuming half the financial resources assigned to a project. The most laborious part of software testing is the generation of test-data. Currently, this process is principally a manual process. Hence, the automation of test-data generation can significantly cut the total cost of software testing and the software development cycle in general. A number of automated test-data generation approaches have already been explored. This paper highlights the goal-oriented approach as a promising approach to devise automated test-data generators. A range of optimization techniques can be used within these goal-oriented test-data generators, and their respective characteristics, when applied to these situations remain relatively unexplored. Therefore, in this paper, a comparative study about the effectiveness of the most commonly used optimization techniques is conducted.

WebPET: An Online Tool for Lexicographic Decision Making

The Web Personal Evaluation Tool (WebPET) helps users select the most suitable information and Web services using a lexicographically based aggregation mechanism. The Internet has become a multiuser, multisource information repository with billions of available documents and millions of users. As the Internet shifts toward services, users will utilize Web services to perform tasks related to their work and pleasure-searching for relevant information, shopping, or looking for entertainment.

Using a web Personal Evaluation Tool – PET for lexicographic multi-criteria service selection

The amount of information stored on the Internet grows every day. Users are forced to deal with an overwhelming number of possibilities, and continuously make decisions if they want to obtain meaningful information or services. The paper describes an approach for a simple yet effective selection of the most suitable information and web services that fit users needs. The novelty of the approach is twofold: the concept of lexicographical preferences is distinctively used for a multi-criteria decision-making; a simple mechanism of representing users criterion satisfaction levels is proposed. The lexicographic preferences allow for mimicking users attitude that some criteria should be satisfied before other criteria are considered. The criterion satisfaction levels are defined with a single threshold that represents a boundary value between acceptable and unacceptable values of attributes of alternatives. The paper includes results of case studies preformed on a prototype of a web selection system built using the proposed approach.

Genetic algorithms for hardware-software partitioning and optimal resource allocation

A scheme for time and power efficient embedded system design, using hardware and software components, is presented. Our objective is to reduce the execution time and the power consumed by the system, leading to the simultaneous multi-objective minimization of time and power. The goal of suitably partitioning the system into hardware and software components is achieved using Genetic Algorithms (GA). Multiple tests were conducted to confirm the consistency of the results obtained and the versatile nature of the objective functions. An enhanced resource constrained scheduling algorithm is used to determine the system performance. To emulate the characteristics of practical systems, the influence of inter-processor communication is examined. The suitability of introducing a reconfigurable hardware resource over pre-configured hardware is explored for the same objectives. The distinct difference in the task to resource mapping with the variation in design objective is studied. Further, the procedure to allocate optimal number of resources based on the design objective is proposed. The implementation is constrained for power and time individually, with GA being used to arrive at the resource count to suit the objective. The results obtained are compared by varying the time and power constraints. The test environment is developed using randomly generated task graphs. Exhaustive sets of tests are performed on the set design objectives to validate the proposed solution.

Application of genetic algorithms to pattern recognition of defects in GIS

A computerized pattern recognition system based on the analysis of phase resolved partial discharge (PRPD) measurements, and utilizing genetic algorithms, is presented. The recognition system was trained to distinguish between basic types of defects appearing in gas-insulated system (GIS), such as voids in spacers, moving metallic particles, protrusions on electrodes, and floating electrodes. The classification of defects is based on 60 measurement parameters extracted from PRPD patterns. Classification of defects appearing in GIS installations is performed using the Bayes classifier combined with genetic algorithms and is compared to the performance of the other classifiers, including minimal-distance, percent score and polynomial classifiers. Tests with a reference database of more than 600 individual measurements collected during laboratory experiments gave satisfactory results of the classification process.