Summary form only given. Unlike desktop systems, embedded systems use different user interface technologies; have significantly smaller form factors; use a wide variety of processors; and have very tight constraints on power/energy consumption, user response time, and physical space. With its platform independence and secure execution environment, Java is fast becoming the language of choice for programming embedded systems. In order to extend its use to array based applications from embedded image and video processing, Java programmers need to employ several optimizations. Unfortunately, due to its precise exception mechanism and bytecode distribution form, it is not generally possible to use classical loop based optimization techniques for array based embedded Java applications. Observing this, this paper proposes a dynamic memory layout optimization strategy for Java applications. The strategy is based on observing the cache behavior dynamically and transforming memory layouts of arrays, when necessary, during the course of execution. This is in contrast to many previously proposed memory layout optimization strategies, which are static in nature (i.e., they are applied at compile time). Our results indicate large performance improvements on a suite of seven array based applications.

The World Wide Web contains a number of source code archives. Programs are usually classified into various categories within the archive by hand. We report on experiments for automatic classification of source code into these categories. We examined a number of factors that affect classification accuracy. Weighting features by expected entropy loss makes a significant improvement in classification accuracy. We show a Support Vector Machine can be trained to classify source code with a high degree of accuracy. We feel these results show promise for software reuse.

There are various source code archives on the World Wide Web. These archives are usually organized by application categories and programming languages. However, manually organizing source code repositories is not a trivial task since they grow rapidly and are very large (on the order of terabytes). We demonstrate machine learning methods for automatic classification of archived source code into eleven application topics and ten programming languages. For topical classification, we concentrate on C and C++ programs from the Ibiblio and the Sourceforge archives. Support vector machine (SVM) classifiers are trained on examples of a given programming language or programs in a specified category. We show that source code can be accurately and automatically classified into topical categories and can be identified to be in a specific programming language class.