Refactoring is a process of applying behavior-preserving transformations to improve the design, readability, structure, performance, abstraction, and maintainability of existing code. This paper presents an approach to genetic algorithm-driven refactoring for Java programs to automatically judge the qualities of programs based on design patterns. If a program is judged to be bad, refactoring will be further recommended so that the program can be transformed using an appropriate design pattern.

To develop Web applications of high quality, it is important to apply efficient frameworks to standardize the process of development in projects, or apply useful design patterns to produce the program code that can easily be enhanced. In addition to these efforts, we have to check the programs to see whether they keep various kinds of rules such as verification items for security which are common for all kinds of Web applications, and verification items for coding styles or code conventions which pertain to each project. This paper proposes a verification method for an extensible syntax-oriented verifier, and describes the implementation of its prototype system, SyntaxVerifier. SyntaxVerifier makes it possible to detect syntactical objects based on their syntactic contexts. It realizes a context-dependent recursive verification which makes it easy to dynamically trace a syntax tree in a verification process itself.

A model for estimating program size, before coding of the program begins, on the basis of the complexity of the software is presented. The complexity of the software is computed from program logic charts with the Halstead metrics. The relationships between the complexity of the software and program size are then statistically analyzed by taking hypothesized environmental factors, such as program categories, into consideration. Results of an experiment show that, of the Halstead metrics, N/sub 1/ is the most significant and reasonable as a metric for computing the complexity of the software from program logic charts. The results also show that the model based on this metric is more reliable than the conventional size estimation method.>

A model is presented for estimating the number of errors remaining in a program at the beginning of the testing phase of development. The relationships between the errors occurring in a program and the various factors that affect software development, such as programmer skill, are statistically analyzed. The model is then derived using the factors significantly identified in the analysis. On the basis of data collected during the development of large-scale software systems, it is shown that factors such as frequency of program specification change, programmer skill, and volume of program design documentation are significant and that the model based on these factors is more reliable than conventional error prediction methods based on program size alone.>