Abstract
Optimizing software to become (more) energy efficient is an important concern for the software industry. Although several techniques have been proposed to measure energy consumption within software engineering, little work has specifically addressed Software Product Lines (SPLs). SPLs are a widely used software development approach, where the core concept is to study the systematic development of products that can be deployed in a variable way, e.g., to include different features for different clients. The traditional approach for measuring energy consumption in SPLs is to generate and individually measure all products, which, given their large number, is impractical. We present a technique, implemented in a tool, to statically estimate the worst-case energy consumption for SPLs. The goal is to reason about energy consumption in all products of a SPL, without having to individually analyze each product. Our technique combines static analysis and worst-case prediction with energy consumption analysis, in order to analyze products in a feature-sensitive manner: a feature that is used in several products is analyzed only once, while the energy consumption is estimated once per product. This paper describes not only our previous work on worst-case prediction, for comprehensibility, but also a significant extension of such work. This extension has been realized in two different axis: firstly, we incorporated in our methodology a simulated annealing algorithm to improve our worst-case energy consumption estimation. Secondly, we evaluated our new approach in four real-world SPLs, containing a total of 99 software products. Our new results show that our technique is able to estimate the worst-case energy consumption with a mean error percentage of 17.3% and standard deviation of 11.2%.

Abstract
In the early days of the world wide web, browsers were developed to navigate through (static) HTML web page documents. This has changed dramatically, and nowadays web pages are dynamic, expressed by programs written in regular programming languages. As a result, browsers are almost operating systems, having to interpret/compile such programs and execute them within the browser itself. Currently, while JavaScript is the main de facto language to express web pages, it does have various short comings and performance inefficiencies. WebAssembly, a new portable and size/load efficient alternative developed by major IT powerhouses, is seen as the future substitute. As WebAssembly aims to be more performance efficient than JavaScript, we aim to look at this current status and present a preliminary study on the performance of these two, based on their runtime and energy efficiency. Preliminary results show that WebAssembly, while still in its infancy, is starting to already challenge JavaScript, with much more room to grow. Additionally, our benchmarking framework is also made available to allow further research and replication.

Abstract
Recent studies show that many real-world software faults are due to slight modifications (mutations) to the program. Thus, analyzing transformations made by a developer and associating them with well-known mutation operators can help pinpoint and repair the root cause of failures. This paper proposes a mutation operator inference technique: given the original program and one of its subsequent forms, it infers which mutation operators would transform the original and produce such a version. Moreover, we implemented this technique as a tool called Morpheus, which analyzes faulty Java programs. We have also validated both the technique and tool by analyzing a repository with 1753 modifications for 20 different programs, successfully inferring mutation operators 78% of times. Furthermore, we also show that several program versions result from not just a single mutation operator but multiple ones. In the end, we resort to real-world case studies to demonstrate the advantages of this approach regarding program repair.

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