Michael M. Richter

A flexible method for software effort estimation by analogy

Effort estimation by analogy uses information from former similar projects to predict the effort for a new project. Existing analogy-based methods are limited by their inability to handle non-quantitative data and missing values. The accuracy of predictions needs improvement as well. In this paper, we propose a new flexible method called AQUA that is able to overcome the limitations of former methods. AQUA combines ideas from two known analogy-based estimation techniques: case-based reasoning and collaborative filtering. The method is applicable to predict effort related to any object at the requirement, feature, or project levels. Which are the main contributions of AQUA when compared to other methods? First, AQUA supports non-quantitative data by defining similarity measures for different data types. Second, it is able to tolerate missing values. Third, the results from an explorative study in this paper shows that the prediction accuracy is sensitive to both the number N of analogies (similar objects) taken for adaptation and the threshold T for the degree of similarity, which is true especially for larger data sets. A fixed and small number of analogies, as assumed in existing analogy-based methods, may not produce the best accuracy of prediction. Fourth, a flexible mechanism based on learning of existing data is proposed for determining the appropriate values of N and T likely to offer the best accuracy of prediction. New criteria to measure the quality of prediction are proposed. AQUA was validated against two internal and one public domain data sets with non-quantitative attributes and missing values. The obtained results are encouraging. In addition, acomparative analysis with existing analogy-based estimation methods was conducted using three publicly available data sets that were used by these methods. Intwo of the three cases, AQUA outperformed all other methods.

Similarity, Uncertainty and Case-Based Reasoning in Patdex

Patdex is an expert system which carries out case-based reasoning for the fault diagnosis of complex machines. It is integrated in the Moltke workbench for technical diagnosis, which was developed at the university of Kaiserslautern over the past years, Moltke contains other parts as well, in particular a model-based approach; in Patdex where essentially the heuristic features are located. The use of cases also plays an important role for knowledge acquisition. In this paper we describe Patdex from a principal point of view and embed its main concepts into a theoretical framework.

On the notion of similarity in case based reasoning and fuzzy theory

Notions of similarity and neighborhood play an important role in informatics. Different disciplines have developed their own treatment of related measures. We consider this problem under the viewpoint of case based reasoning and fuzzy theory. While distance and similarity can be considered to be formally equivalent, there exist some differences concerning their intuitive use which have impact on the composition of global measures from local ones.

Case-based reasoning foundations

A basic observation is that case-based reasoning has roots in different disciplines: cognitive science, knowledge representation and processing, machine learning and mathematics. As a consequence, there are foundational aspects from each of these areas. We briefly discuss them and comment on the relations between these types of foundations.

On the Notion of Similarity in Case-Based Reasoning

The semantics of similarity measures is studied and reduced to the evidence theory of Dempster and Shafer. Applications are given for classification and configuration, the latter uses utility theory in addition.

Image processing in case-based reasoning

This commentary summarizes case-based reasoning (CBR) research applied to image processing. It includes references to the systems, workshops, and landmark publications. Image processing includes a variety of image formats, from computer tomography images to remote sensing and spatial data sets.

Adaptivity and Learning — an Interdisciplinary Debate

Adaptivity and learning have received much attention from a variety of scientific disciplines in recent decades. In fact, adaptivity and learning as scientific concerns seem to be unusual in their scope, as they play a role in producing far-reaching discoveries, guiding experiments and theoretical approaches, and enabling real-life applications. These issues have arisen in mathematics, physics, biology, informatics, economics, and other fields more or less simultaneously. That same development continued in subdisciplines such as statistics and statistical mechanics, logic and computability theory, game theory and optimization, neurobiology of particular species, human brain research, and robotics.

ARC-TEC : acquisition, representation and compilation of technical knowledge

A global description of an expert system shell for the domain of mechanical engineering is presented. The ARC-TEC project constitutes an AI approach to realize the CIM idea. Along with conceptual solutions, it provides a continuous sequence of software tools for the acquisition, representation and compilation of technical knowledge. The shell combines the KADS knowledge-acquisition methodology, the KL-ONE representation theory and the WAM compilation technology. For its evaluation a prototypical expert system for production planning is developed. A central part of the system is a knowledge base formalizing the relevant aspects of common sense in mechanical engineering. Thus, ARC-TEC is less general than the CYC project but broader than specific expert systems for planning or diagnosis.

The search for knowledge, contexts, and Case-Based Reasoning

A major goal of this paper is to compare Case-Based Reasoning with other methods searching for knowledge. We consider knowledge as a resource that can be traded. It has no value in itself; the value is measured by the usefulness of applying it in some process. Such a process has info-needs that have to be satisfied. The concept to measure this is the economical term utility. In general, utility depends on the user and its context, i.e., it is subjective. Here, we introduce levels of contexts from general to individual. We illustrate that Case-Based Reasoning on the lower, i.e., more personal levels CBR is quite useful, in particular in comparison with traditional informational retrieval methods.

Color image segmentation guided by a color gradient network

Existing region-growing segmentation algorithms are mainly based on a static similarity concept, where only homogeneity of pixels or textures within a region plays a role. Typical natural scenes, however, show strong continuous variations of color, presenting a different, dynamic order that is not captured by existing algorithms which will segment a sky with different intensities and hues of blues or an irregularly illuminated surface as a set of different regions. We present and validate empirically a new, extremely simple approach that shows very satisfying results when applied on such scenes, while not showing poorer performance than traditional methods when applied to standard region-growing problems.