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Software developers are more and more eager to understand their code's energy performance. However, even with such knowledge it is difficult to know how to improve the code. Indeed, little tool support exists to understand the energy consumption profile of a software system and to eventually (automatically) improve its code. In this paper we present a tool termed jStanley which automatically finds collections in Java programs that can be replaced by others with a positive impact on the energy consumption as well as on the execution time. In seconds, developers obtain information about energy-eager collection usage. jStanley will further suggest alternative collections to improve the code, making it use less time, energy, or a combination of both. The preliminary evaluation we ran using jStanley shows energy gains between 2% and 17%, and a reduction in execution time between 2% and 13%. A video can be seen at https://greensoftwarelab.github.io/jStanley. © 2018 Association for Computing Machinery.

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Our department has long been an advocate of the functional-first school of programming and has been teaching Haskell as a first language in introductory programming course units for 20 years. Although the functional style is largely beneficial, it needs to be taught in an enthusiastic and captivating way to fight the unusually high computer science drop-out rates and appeal to a heterogeneous population of students. This paper reports our experience of restructuring, over the last 5 years, an introductory laboratory course unit that trains hands-on functional programming concepts and good software development practices. We have been using game programming to keep students motivated, and following a methodology that hinges on test-driven development and continuous bidirectional feedback. We summarise successes and missteps, and how we have learned from our experience to arrive at a model for comprehensive and interactive functional game programming assignments and a general functionally-powered automated assessment platform, that together provide a more engaging learning experience for students. In our experience, we have been able to teach increasingly more advanced functional programming concepts while improving student engagement.

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We give a language-based security treatment of domain-specific languages and compilers for secure multi-party computation, a cryptographic paradigm that. enables collaborative computation over encrypted data. Computations are specified in a core imperative language, as if they were intended to be executed by a trusted-third party, and formally verified against. an information-flow policy modelling (an upper bound to) their leakage. This allows non-experts to assess the impact of performance driven authorized disclosure of intermediate values. Specifications are then compiled to multi-party protocols. We formalize protocol security using (distributed) probabilistic information-flow and prove security-preserving compilation: protocols only leak what. is allowed by the source policy. The proof exploits a natural but previously missing correspondence between simulation-based cryptographic proofs and (composable) probabilistic non-interference. Finally, we extend our framework to justify leakage cancelling, a domain-specific optimization that allows to first write an efficient specification that fails to meet the allowed leakage upper-bound, and then apply a probabilistic preprocessing that brings leakage to the acceptable range.