Abstract
This article introduces the E-MANAFA energy profiler, a plug-and-play, device-independent, model-based profiler capable of obtaining fine-grained energy measurements on Android devices. Besides having the capability to calculate performance metrics such as the energy consumed and runtime during a time interval, E-MANAFA also allows to estimate the energy consumed by each device component (e.g. CPU, WI-FI, screen). In this article, we present the main elements that compose this framework, as well as its workflow. In order to present the power of this tool, we demonstrate how the tool can measure the overhead of the instrumentation technique used in the PyAnaDroid application benchmarking pipeline, which already supports E-MANAFA to monitor power consumption in its Android application automatic execution process. Video demo: shorturl.at/hmyz5

Abstract
This paper presents PyAnaDroid, an open-source, fully-customizable execution pipeline designed to benchmark the performance of Android native projects and applications, with a special emphasis on benchmarking energy performance. PyAnaDroid is currently being used for developing large-scale mobile software empirical studies and for supporting an advanced academic course on program testing and analysis. The presented artifact is an expandable and reusable pipeline to automatically build, test and analyze Android applications. This tool was made openly available in order to become a reference tool to transparently conduct, share and validate empirical studies regarding Android applications. This document presents the architecture of PyAnaDroid, several use cases, and the results of a preliminary analysis that illustrates its potential. Video demo: https://youtu.be/7AV3nrh4Qc8

Abstract
Sustainable development has become an increasingly important theme not only in the world politics, but also an increasingly central theme for the engineering professions around the world. Software engineers are no exception as shown in various recent research studies. Despite the intensive research on green software, today’s undergraduate computing education often fails to address our environmental responsibility. In this paper, we present a module on energy efficient software that we introduced as part of an advanced course on software analysis and testing. In this module students study techniques and tools to analyze and optimize energy consumption of software systems. Preliminary results of the first four instances of this course show that students are able to optimize the energy consumption of software systems.

Abstract
Software performance concerns have been attracting research interest at an increasing rate, especially regarding energy performance in non-wired computing devices. In the context of mobile devices, several research works have been devoted to assessing the performance of software and its underlying code. One important contribution of such research efforts is sets of programming guidelines aiming at identifying efficient and inefficient programming practices, and consequently to steer software developers to write performance-friendly code.Despite recent efforts in this direction, it is still almost unfeasible to obtain universal and up-to-date knowledge regarding software and respective source code performance. Namely regarding energy performance, where there has been growing interest in optimizing software energy consumption due to the power restrictions of such devices. There are still many difficulties reported by the community in measuring performance, namely in large-scale validation and replication. The Android ecosystem is a particular example, where the great fragmentation of the platform, the constant evolution of the hardware, the software platform, the development libraries themselves, and the fact that most of the platform tools are integrated into the IDE's GUI, makes it extremely difficult to perform performance studies based on large sets of data/applications. In this paper, we analyze the execution of a diversified corpus of applications of significant magnitude. We analyze the source-code performance of 1322 versions of 215 different Android applications, dynamically executed with over than 27900 tested scenarios, using state-of-the-art black-box testing frameworks with different combinations of GUI inputs. Our empirical analysis allowed to observe that semantic program changes such as adding functionality and repairing bugfixes are the changes more associated with relevant impact on energy performance. Furthermore, we also demonstrate that several coding practices previously identified as energy-greedy do not replicate such behavior in our execution context and can have distinct impacts across several performance indicators: runtime, memory and energy consumption. Some of these practices include some performance issues reported by the Android Lint and Android SDK APIs. We also provide evidence that the evaluated performance indicators have little to no correlation with the performance issues' priority detected by Android Lint. Finally, our results allowed us to demonstrate that there are significant differences in terms of performance between the most used libraries suited for implementing common programming tasks, such as HTTP communication, JSON manipulation, image loading/rendering, among others, providing a set of recommendations to select the most efficient library for each performance indicator. Based on the conclusions drawn and in the extension of the developed work, we also synthesized a set of guidelines that can be used by practitioners to replicate energy studies and build more efficient mobile software.

Abstract
This tutorial aims to provide knowledge on a different facet of efficiency in data structures: energy efficiency. As many recent studies have shown, the main roadblock in regards to energy efficient software development are the misconceptions and heavy lack of support and knowledge, for energy-aware development, that programmers have. Thus, this tutorial aims at helping provide programmers more knowledge pertaining to the energy efficiency of data structures. We conducted two in-depth studies to analyze the performance and energy efficiency of various data structures from popular programming languages: Haskell and Java. The results show that within the Haskell programming language, the correlation between performance and energy consumption is statistically almost identical, while there are cases with more variation within the Java language. We have presented which data structures are more efficient for common operations, such as inserting and removing elements or iterating over the data structure. The results from our studies can help support developers in better understanding such differences within data structures, allowing them to carefully choose the most adequate implementation based on their requirements and goals. We believe that such results will help further close the gap when discussing the lack of knowledge in energy efficient software development.

Abstract
Type systems are responsible for assigning types to terms in programs. That way, they enforce the actions that can be taken and can, consequently, detect type errors during compilation. However, while they are able to flag the existence of an error, they often fail to pinpoint its cause or provide a helpful error message. Thus, without adequate support, debugging this kind of errors can take a considerable amount of effort. Recently, neural network models have been developed that are able to understand programming languages and perform several downstream tasks. We argue that type error debugging can be enhanced by taking advantage of this deeper understanding of the language's structure. In this paper, we present a technique that leverages GPT-3's capabilities to automatically fix type errors in OCaml programs. We perform multiple source code analysis tasks to produce useful prompts that are then provided to GPT-3 to generate potential patches. Our publicly available tool, Mentat, supports multiple modes and was validated on an existing public dataset with thousands of OCaml programs. We automatically validate successful repairs by using Quickcheck to verify which generated patches produce the same output as the user-intended fixed version, achieving a 39% repair rate. In a comparative study, Mentat outperformed two other techniques in automatically fixing ill-typed OCaml programs.