Automatic Labeling of Software Components and their Evolution using Log-Likelihood Ratio of Word Frequencies in Source Code

As more and more open-source software components become available on the internet we need automatic ways to label and compare them. For example, a developer who searches for reusable software must be able to quickly gain an understanding of retrieved components. This understanding cannot be gained at the level of source code due to the semantic gap between source code and the domain model. In this paper we present a lexical approach that uses the log-likelihood ratios of word frequencies to automatically provide labels for software components. We present a prototype implementation of our labeling/comparison algorithm and provide examples of its application. In particular, we apply the approach to detect trends in the evolution of a software system.

Explora: Infrastructure for Scaling Up Software Visualisation to Corpora

Visualisation provides good support for software analysis. It copes with the intangible nature of software by providing concrete representations of it. By reducing the complexity of software, visualisations are especially useful when dealing with large amounts of code. One domain that usually deals with large amounts of source code data is empirical analysis. Although there are many tools for analysis and visualisation, they do not cope well software corpora. In this paper we present Explora, an infrastructure that is specifically targeted at visualising corpora. We report on early results when conducting a sample analysis on Smalltalk and Java corpora.