9759378
Noun Sense Disambiguation with WordNet for Software Design Retrieval
Natural language understanding can be used to improve the usability of intelligent Computer Aided Software Engineering (CASE) tools. For a software designer it can be helpful in two ways: a broad range of natural language terms in the naming of software objects, attributes and methods can be used; and the system is able to understand the meaning of these terms so that it could use them in reasoning mechanisms like information retrieval. But, the problem of word sense disambiguation is an obstacle to the development of computational systems that can fully understand natural language. In order to deal with this problem, this paper presents a word sense disambiguation method and how it is integrated with a CASE tool.
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.
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).
16219760
Selection and Reuse of Software Design Patterns Using CBR and WordNet
Software engineers and programmers deal with repeated problems and situations in the course of software design. This lead to the development of software design patterns, which can be defined as a description of an abstract solution for abstract design problems. Existing approaches to pattern application using computer tools, need the help and guidance of a human designer to select which design pattern to apply. The automation of this task opens the possibility of CASE design tools providing complete automation for the application of design patterns, and the offering new functionalities that can help the software designer to improve systems, and do better software reuse. In this paper we present an approach that automates design pattern selection and application. This approach is based on CaseBased Reasoning and WordNet, showing how they are combined to generate evolved software design diagrams. We also present an experimental study of our approach.
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.
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.
5245012
A Conditional Random Field for Discriminatively-trained Finite-state String Edit Distance
The need to measure sequence similarity arises in information extraction, object identity, data mining, biological sequence analysis, and other domains. This paper presents discriminative string-edit CRFs, a finite-state conditional random field model for edit sequences between strings. Conditional random fields have advantages over generative approaches to this problem, such as pair HMMs or the work of Ristad and Yianilos, because as conditionally-trained methods, they enable the use of complex, arbitrary actions and features of the input strings. As in generative models, the training data does not have to specify the edit sequences between the given string pairs. Unlike generative models, however, our model is trained on both positive and negative instances of string pairs. We present positive experimental results on several data sets.
8238424
GVR: A New Genetic Representation for the Vehicle Routing Problem
In this paper we analyse a new evolutionary approach to the vehicle routing problem. We present Genetic Vehicle Representation (GVR), a two-level representational scheme designed to deal in an effective way with all the information that candidate solutions must encode. Experimental results show that this method is both effective and robust, allowing the discovery of new best solutions for some well-known benchmarks.
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.
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.
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.