Java program execution times vary greatly with different garbage collection algorithms. Until now, it has not been possible to determine the best GC algorithm for a particular program without exhaustively profiling that program for all available GC algorithms. This paper presents a new approach. We use machine learning techniques to build a prediction model that, given a single profile run of a previously unseen Java program, can predict a good GC algorithm for that program. We implement this technique in Jikes RVM and test it on several standard benchmark suites. Our technique achieves 5 % speedup in overall execution time (averaged across all test programs for all heap sizes) compared with selecting the default GC algorithm in every trial. We present further experiments to show that an oracle predictor could achieve an average 17 % speedup on the same experiments. In addition, we provide evidence to suggest that GC behaviour is sometimes independent of program inputs. These observations lead us to propose that intelligent selection of GC algorithms is suitably straightforward, efficient and effective to merit further exploration regarding its potential inclusion in the general Java software deployment process. Categories and Subject Descriptors D.3.4 [Programming Languages]: Processors—Memory management (garbage collection)

Abstract. The open source software ecosystem comprises more than a hundred thousand applications of varying quality. Individuals and organizations wishing to use open source software packages have scarce objective data to evaluate their quality. However, open source development projects by definition allow anybody to read, and therefore evaluate their source code. In addition, most projects also publish process-related artefacts, such as bug databases, mailing lists, and configuration management system logs. The software quality observatory is a platform that uses these product and process data sources to automatically evaluate the quality of open source projects. A plugin-based service-oriented architecture allows the mixing and matching of metrics extraction suites, source code repositories, and transformation filters. The resulting platform is aimed at it consultants and managers, the open source community, and researchers. 1