11166519
Case Retrieval of Software Designs using WordNet
Software design is one of the most important phases in system development, due to crucial decisions that are made during this phase. The need for software being developed in less time puts a lot of pressure in the design phase. One way to solve this problem is to reuse previous design solutions. In software design reuse the retrieval of relevant designs is a key issue. 
 
Case-Based Reasoning reuses past experiences to solve new problems, providing a reasoning framework for design reuse. But designing software involves reasoning at a more abstract level than coding software, thus a software design reuse tool must be able to work with a broad range of abstract concepts. A possible solution is the use of a common sense ontology, capable of providing this kind of knowledge, otherwise the system would have to demand a lot of knowledge from the designer. 
 
This paper presents an approach to software design retrieval based on Case-Based Reasoning combined with a common sense ontology - WordNet. We describe the case retrieval algorithm, the case similarity metrics and experimental results.
15250271
Evaluation of Case-Based Maintenance Strategies in Software Design
CBR applications running in real domains can easily reach thousands of cases, which are stored in the case library. Retrieval times can increase greatly if the retrieval algorithm can not cope with such an amount of cases. Redundancy can also be a problem, focusing retrieval alternatives in a very restricted search space. Basically, the system's performance starts to degrade with the increase of the case-base size. Case-base maintenance allows CBR systems to deal with this problem, mainly through the use of case selection criteria. In this paper we present an experimental study about several case-base maintenance policies developed till now. We adapted and implemented these policies to a CBR system for software reuse and design, testing the applicability of these policies to cases with a complex representation (combination of tree and graph representations).
33993842
Using WordNet for case-based retrieval of UML models
Software complexity has increased substantially in the last decade. This has made software development teams work faster and under tight budgets. Reusing software can be a way of solving this problem. Companies should reuse previous working solutions in new projects, decreasing the development time and increasing software quality. But there are at least two requirements to implement this solution. One is that the company must have a central knowledge repository with software specifications, designs and code from previous system developments. The second one is to have at the disposal tools capable of using this repository in an intelligent way. We have developed a system capable of providing these requirements. It has a central knowledge base that can be used through Case-Based Reasoning. The knowledge base integrates a common ontology called WordNet. providing classification for software objects. This paper focuses on the retrieval of design models using the combination of WordNet and Case-Based Reasoning. We also present a retrieval example, and experimental work showing the performance of the retrieval and ranking mechanisms.
15139022
Darwinci: Creating Bridges to Creativity
This paper presents Darwinci, a system that generates new ideas, using a multi-domain knowledge base composed by musical and drawing structures. Its theoretical background comes from the theory of Divergent Production as stated in [7], Genetic Algorithms and metaphor interpretation. Rather than attempting to model human thought, Darwinci’s main goal is to make cross-domain transfer of ideas, in order to create something new, thus becoming a good proposal for creative problem solving.
11219736
Combining Case-Based Reasoning and Analogical Reasoning in Software Design
Designers use several types of knowledge and reasoning mechanisms during the creation of new artefacts. In order to cope with this cognitive characteristic of design, an intelligent design tool able to help a designer must integrate several reasoning mechanisms and knowledge formats. Case-based reasoning and analogical reasoning are usually considered as two distinct mechanisms, though they are also considered to be in the same cognitive axis, case-based reasoning being in one extreme, and analogy in the other. Both are important reasoning mechanisms in the generation of new designs, but they both reflect different ways of exploring the design space. In this paper we present a way of combining both techniques, showing how it was integrated in an intelligent software design tool. Experimental results are presented and discussed, showing the advantages and limitations of this approach.
18713951
Using CBR for Semantic Analysis of Software Specifications
Helping software designers in their task implies the development of tools with intelligent capabilities. One such capability is the integration of natural language understanding in CASE tools, thus improving the designer/tool communication. In this paper, we present a CBR approach for the generation of UML class diagrams from natural language text. This approach is implemented in a CASE tool, with the goal of helping the software designer create the first system model. We describe the natural language conversion module and give an overview of the tool in which it is integrated. Experimental results for retrieval and adaptation mechanisms are also presented.
6376797
Sharing engineering information and knowledge
This paper describes an agent-based recommendation system developed to support knowledge acquisition and sharing processes. Its purpose is to aid the process of community building, specially in the early phase of peer search and recognition in a distributed engineering environment, where multidisciplinary teams must be established. In this kind of problem information about possible candidates to make up the teams must be gathered from all available channels, including the electronic ones. Due to privacy issues, Web based data mining systems are not efficient for the purpose of constructing candidate's profiles, even if they can offer some help; therefore a distributed solution that permits each candidate's workplace to be explored was developed. As a means of aggregating collected knowledge for sharing, a conceptual model, relating candidate's profiles and their knowledge domain was developed in the form of an ontology. This strategy allows the system to be specialised in terms of the candidates' main knowledge domain.
10111787
Golomb Rulers: The Advantage of Evolution
In this paper we present a new evolutionary algorithm designed to efficiently search for optimal Golomb rulers. The proposed approach uses a redundant random keys representation to codify the information contained in a chromosome and relies on a simple interpretation algorithm to obtain feasible solutions. Experimental results show that this method is successful in quickly identifying good solutions and that can be considered as a realistic alternative to massive parallel approaches that need several months or years to discover high quality Golomb rulers.
2344862
Busy Beaver - The Influence of Representation
The Busy Beaver is an interesting theoretical problem proposed by Rado in 1962, in the context of the existence of non computable functions. In this paper we propose an evolutionary approach to this problem. We will focus on the representational issues, proposing alternative ways of codifying and interpreting Turing Machines, designed to take advantage of the existence of sets of equivalent Turing Machines. The experimental results show that these alternatives provide improvement over the "standard" genetic codification.
13936575
Shallow Parsing with Conditional Random Fields
Conditional random fields for sequence labeling offer advantages over both generative models like HMMs and classifiers applied at each sequence position. Among sequence labeling tasks in language processing, shallow parsing has received much attention, with the development of standard evaluation datasets and extensive comparison among methods. We show here how to train a conditional random field to achieve performance as good as any reported base noun-phrase chunking method on the CoNLL task, and better than any reported single model. Improved training methods based on modern optimization algorithms were critical in achieving these results. We present extensive comparisons between models and training methods that confirm and strengthen previous results on shallow parsing and training methods for maximum-entropy models.
35936505
Finding Manufacturing Expertise Using Ontologies and Cooperative Agents
The new product development process, in its early phases, congregates knowledge from diverse domains and sources, spread among specialists that usually are not aware of each other’s research interests. This work presents a tool designed to make the establishment of new communities or teams easier. The tool organizes their informal vocabulary into formal domain categories, in order to correlate them, and advises its users of potential networks they could belong to. An approach based on ontologies and machine learning is used to structure and make knowledge acquisition / retrieval, together with agents that cooperate and coordinate the access to the knowledge. The tool automatically updates its knowledge base relieving the user from tasks she/he is unfamiliar with. Initial experiments demonstrated the potential of this kind of tool. A test case is being developed encompassing members of a scientific network in the manufacturing area. Results will be analyzed envisaging its broader use and application to different knowledge domains.