Yoshio Kataoka

A quantitative evaluation of maintainability enhancement by refactoring

Program refactoring is a technique to enhance the maintainability of a program. Although the concept itself is considered to be effective, there are few quantitative evaluation of its impact to the software maintainability. It is sometimes difficult to judge whether the refactoring in question should be applied or not without knowing the effect accurately. We propose a quantitative evaluation method to measure the maintainability enhancement effect of program refactoring. We focused on the coupling metrics to evaluate the refactoring effect. By comparing the coupling before and after the refactoring, we could evaluate the degree of maintainability enhancement. We applied our method to a certain program and showed that our method was really effective to quantify the refactoring effect and helped us to choose appropriate refactorings.

Evaluating software maintenance cost using functional redundancy metrics

Source code copying for reuse (code cloning) is often observed in software implementations. Such code cloning causes difficulty when software functionalities are modified: i.e, cloned codes increase the maintenance cost of software. We aim to estimate the maintenance cost caused by clones. We propose a novel approach, which evaluates influence of cloned codes over the maintenance cost. The basic idea is to measure functional redundancy (FR): a degree of propagation of clone-potential functions. FR is measured as follows: first, we cluster functions in the software according to similarities between them. Second, we make an n-ary weighted tree(FR tree) based on the cluster. Finally, we measure FR by weight of each node in FR-tree. In this paper, we describe the details of our proposal. We also apply the approach to 17K-ELOC C code to demonstrate its effectiveness.

ARVE: aspect-oriented runtime verification environment

Software quality assurance activities consume a large amount of effort in industrial software developments. Actually, industrial software development sometimes requires a larger amount of testing/verification assets than the product code itself. Appropriate management of the testing/verification assets will effectively reduce the software quality assurance effort. We propose a verification asset reuse environment based on the aspect-oriented programming paradigm. Our tool, ARVE (Aspect-oriented Runtime Verification Environment), enables efficient verification asset reuse thanks to the aspect-oriented scripting language. ARVE also promotes the efficiency of the verification process by automating the verification script weaving.

An approach to reverse quality assurance with data-oriented program analysis

The paper describes a quality assurance approach for source code to assure source code quality with less effort than conventional method. The paper introduces the data relation tracking method (DRTM) as a practical instance of data oriented program analysis, a key factor of our new quality assurance approach. DRTM helps comprehension of the source code by extracting the internal logic of the source code in declarative notation. An example and evaluation of DRTM for the C language is also described. The example shows that DRTM can deal in data structures and inter-data relations of any control structure. The evaluation shows that DRTM can extract the internal logic of the source code uniquely and that the extracted internal logic is useful for quality assurance of the source code.