Automatic Collection Selection using Machine Learning

Most recent programming languages include a collection framework as part of their standard library (or runtime). Examples are Java, C#, Python and Ruby. The Java Collection Framework provides a number of collection classes, some of which implement the same abstract data type, which makes them interchangeable. Developers can therefore choose between several functionally equivalent options. Since collections have different performance characteristics, and may be allocated in thousands of programs locations, the choice of collection has an important impact on performance. Unfortunately, programmers often make sub-optimal choices when picking their collections.In this licentiate thesis, we consider the problem of building automated tooling, which would help the developer choose among several collection implementations. We consider an existing tool called Brainy, which targets C++, and adapt it to the Java context. In doing so, we investigate how to synthesize benchmarks and analyze their behavior to create training data for automated classification. We propose one new generative model for collection benchmarks and present the challenges that porting JBrainy to Java entails. Finally, we compare JBrainy's suggestions versus greedy search, on five well known benchmarks. Our investigations show that JBrainy's suggestions were almost as effective than those of greedy search in minimizing the running time of programs. However, we also find that Brainy's benchmark synthesis methods do not apply well to the Java context, since they introduce some significant biases

Green Security Plugin for Pervasive Computing using the HADAS toolkit

Energy is a critical resource in pervasive computing devices. However, information about energy consumption is not directly accessible through software development environments, making it difficult to reuse the knowledge provided by existing energy-consumption experimental studies. To address this limitation, this paper presents a solution to enrich Android Studio with energy consumption information. We have developed a Green Security Plugin that provides energy-aware information to developers that make use of Android Security API. This plugin has been developed taking advantage of the functionalities provided by the HADAS toolkit. HADAS is a repository of energy consuming concerns in which researchers can store the energy measures obtained during their experimental studies and developers can perform a sustainability analysis to make green design/implementation decisions.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tec

On the Challenges of Software Performance Optimization with Statistical Methods

Most recent programming languages, such as Java, Python and Ruby, include a collection framework as part of their standard library (or runtime). The Java Collection Framework provides a number of collection classes, some of which implement the same abstract data type, making them interchangeable. Developers can therefore choose between several functionally equivalent options. Since collections have different performance characteristics, and can be allocated in thousands of programs locations, the choice of collection has an important impact on performance. Unfortunately, programmers often make sub-optimal choices when selecting their collections.In this thesis, we consider the problem of building automated tools that would help the programmer choose between different collection implementations. We divide this problem into two sub-problems. First, we need to measure the performance of a collection, and use relevant statistical methods to make meaningful comparisons. Second, we need to predict the performance of a collection with as little benchmarking as possible.To measure and analyze the performance of Java collections, we identify problems with the established methods, and suggest the need for more appropriate statistical methods, borrowed from Bayesian statistics. We use these statistical methods in a reproduction of two state-of-the-art dynamic collection selection approaches: CoCo and CollectionSwitch. Our Bayesian approach allows us to make sound comparisons between the previously reported results and our own experimental evidence.We find that we cannot reproduce the original results, and report on possible causes for the discrepancies between our results and theirs.To predict the performance of a collection, we consider an existing tool called Brainy. Brainy suggests collections to developers for C++ programs, using machine learning. One particularity of Brainy is that it generates its own training data, by synthesizing programs and benchmarking them. As a result Brainy can automatically learn about new collections and new CPU architectures, while other approaches required an expert to teach the system about collection performance. We adapt Brainy to the Java context, and investigate whether Brainy's adaptability also holds for Java. We find that Brainy's benchmark synthesis methods do not apply well to the Java context, as they introduce some significant biases. We propose a new generative model for collection benchmarks and present the challenges that porting Brainy to Java entails

Spectroscopic Analysis in the Virtual Observatory Environment with SPLAT-VO

SPLAT-VO is a powerful graphical tool for displaying, comparing, modifying and analyzing astronomical spectra, as well as searching and retrieving spectra from services around the world using Virtual Observatory (VO) protocols and services. The development of SPLAT-VO started in 1999, as part of the Starlink StarJava initiative, sometime before that of the VO, so initial support for the VO was necessarily added once VO standards and services became available. Further developments were supported by the Joint Astronomy Centre, Hawaii until 2009. Since end of 2011 development of SPLAT-VO has been continued by the German Astrophysical Virtual Observatory, and the Astronomical Institute of the Academy of Sciences of the Czech Republic. From this time several new features have been added, including support for the latest VO protocols, along with new visualization and spectra storing capabilities. This paper presents the history of SPLAT-VO, it's capabilities, recent additions and future plans, as well as a discussion on the motivations and lessons learned up to now.Comment: 15 pages, 6 figures, accepted for publication in Astronomy & Computin