Abstract not found

Object-oriented visualization-based software systems for biomedical data analysis must deal with complex and voluminous datasets within a flexible yet intuitive graphical user interface. In a research environment, the development of such systems are difficult to manage due to rapidly changing requirements, incorporation of newly developed algorithms, and the needs imposed by a diverse user base. One issue that research supervisors must contend with is an assessment of the quality of the system's software objects with respect to their extensibility, reusability, clarity, and efficiency.

Effectiveness and clarity of software objects, their adherence to coding standards and programming habits of programmers are important features of overall quality of software systems. This paper proposes an approach towards a quantitative software quality assessment with respect to extensibility, reusability, clarity and efficiency. It exploits techniques of Computational Intelligence (CI) that are treated as a consortium of granular computing, neural networks and evolutionary techniques. In particular, we take advantage of self-organizing maps to gain a better insight into the data, and study genetic decision trees -- a novel algorithmic framework to carry out classification of software objects with respect to their quality. Genetic classifiers serve as a "quality filter" for software objects. Using these classifiers, a system manager can predict quality of software objects and identify low quality objects for review and possible revision. The approach is applied to an object-oriented visualization-based software system for biomedical data analysis.

Product lines evolve out of existing products. In order to evaluate the potential of creating a product line from existing products it is necessary to ‘mine ’ their architectures and analyze the commonalities and variabilities across those architectures. To manage the evaluation process in a disciplined way this paper introduces the MAP (Mining Architectures for Product lines) method. MAP outlines a bottom-up approach for mining the architecture of the existing products, a top-down approach to mapping architectural styles and attributes onto the mined architectures and an approach to analyzing their commonalities and variabilities. It combines well-known architecture reconstruction and product line analysis techniques. A case study is presented showing the application of the method and its benefits are outlined.

Approved for public release; distribution is unlimited. This thesis was completed in cooperation with the Institute for Information Superiority and Innovation.

Abstract—In spite of all advancement in software testing, debugging remains a labor-intensive, manual, time consuming, and error prone process. A candidate solution to enhance debugging process is to fuse it with testing process. To achieve this integration, a possible solution may be categorizing common software tests and errors followed by the effort on fixing the errors through general solutions for each test/error pair. Our approach to address this issue is based on Christopher Alexander’s pattern and pattern language concepts. The patterns in this language are grouped into three major sections and connect the three concepts of test, error, and debug. These patterns and their hierarchical relationship shape a pattern language that introduces a solution to solve software errors in a known testing context. Finally, we will introduce our developed framework ADE as a sample implementation to support a pattern of proposed language, which aims to automate the whole process of evolving software design via evolutionary methods.

Abstract: Business Intelligence (BI) has become very much commoditized nowadays. The big players on the BI scene are now trying to get an edge by offering packaged complete BI solutions that include source to target mappings and ETL processes, metadata modeling and end user BI objects such as dashboards, scorecards, cubes and reports. This article will define out of the box BI solutions, will talk about the pros and cons of these solutions, will present the current state of the existing solutions and will present the development life cycle to create an open source, out of the box, high reliable BI solution.