For past several years, microarray technology has attracted tremendous interest for both scientific community and industry. Recently, the applications of microarrays include gene discovery, disease diagnosis and prognosis, drug discovery, etc. High dimensional data with small sample size is the main problem that generate the application of dimension reduction in microarray data analysis. It is seen that SVM, ANN and NB have recently gained wide popularity for cancer classification problems. An efficient and reliable method of dimension reduction plays an important role to improve the performance of SVM, ANN and NB, when applied for classification of high dimensional microarray data. In this book, we applied different combinations of feature selection / extraction methods, as a novel hybrid dimension reduction method for SVM, ANN and NB classifiers. The obtained results are compared with other popular published dimension reduction methods for SVM, NB and ANN classifiers.

Machine Learning Approach

Little is known about the specific kinds of questions programmers ask when evolving a code base and how well existing tools support those questions. To better support the activity of programming, answers are needed to three broad research questions: 1) What does a programmer need to know about a code base when evolving a software system? 2) How does a programmer go about finding that information? 3) How well do existing tools support programmers in answering those questions? We undertook two qualitative studies of programmers performing change tasks to provide answers to these questions. In this paper, we report on an analysis of the data from these two user studies. This paper makes three key contributions. The first contribution is a catalog of 44 types of questions programmers ask during software evolution tasks. The second contribution is a description of the observed behavior around answering those questions. The third contribution is a description of how existing deployed and proposed tools do, and do not, support answering programmers' questions.

/pdf/asking-and-answering-questions-during-a-programming-change-21o3wehtmb.pdf

Asking and Answering Questions during a Programming Change Task

Concept assignment is a process of recognizing concepts within a computer program and building up an understanding of the program by relating the recognized concepts to portions of the program, its operational context and to one another. The problem of discovering individual human oriented concepts and assigning them to their implementation oriented counterparts for a given program is the concept assignment problem. The authors argue that the solution to this problem requires methods that have a strong plausible reasoning component. They illustrate these ideas through example scenarios using an existing design recovery system called DESIRE. DESIRE is evaluated based on its usage on real-world problems over the years. >

/pdf/the-concept-assignment-problem-in-program-understanding-i6ulb9uy28.pdf

The concept assignment problem in program understanding

Focuses on investigating the combined use of semantic and structural information of programs to support the comprehension tasks involved in the maintenance and reengineering of software systems. "Semantic information" refers to the domain-specific issues (both the problem and the development domains) of a software system. The other dimension, structural information, refers to issues such as the actual syntactic structure of the program, along with the control and data flow that it represents. An advanced information retrieval method, latent semantic indexing, is used to define a semantic similarity measure between software components. Components within a software system are then clustered together using this similarity measure. Simple structural information (i.e. the file organization) of the software system is then used to assess the semantic cohesion of the clusters and files with respect to each other. The measures are formally defined for general application. A set of experiments is presented which demonstrates how these measures can assist in the understanding of a nontrivial software system, namely a version of NCSA Mosaic.

/pdf/supporting-program-comprehension-using-semantic-and-5126ad9aud.pdf

Supporting program comprehension using semantic and structural information

Component recovery and remodularization is a means to get back control on large and complex legacy systems suffering from ad-hoc changes by recovering logical components and restructuring the physical components accordingly to decrease coupling among components and increase cohesion of components. This thesis is on unifying, quantitatively and qualitatively evaluating, improving, and integrating automatic and semi-automatic methods for component recovery.

Atomic Architectural Component Recovery for Program Understanding and Evolution.

Automatic program analysis is regarded here as both the mechanized process of understanding high-level concepts from program text and the use of those concepts to guide program maintenance. A knowledge-based program analysis tool called PAT, which realizes this concept, is described. PAT uses an object-oriented framework to represent programming concepts and a heuristic-based concept-recognition mechanism to derive high-level functional concepts from the source code. As an example, a segment of a much larger program written in a Pascal-like language is analyzed using PAT. >

Knowledge-based program analysis

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Knowledge-based program analysis