Abstract
Data analysis has emerged as a cornerstone in facilitating informed decision-making across myriad fields, in particular in software development and project management. This integrative practice proves instrumental in enhancing operational efficiency, cutting expenditures, mitigating potential risks, and delivering superior results, all while sustaining structured organization and robust control. This paper presents ITC, a synergistic platform architected to streamline multi-organizational and multi-workspace collaboration for project management and technical documentation. ITC serves as a powerful tool, equipping users with the capability to swiftly establish and manage workspaces and documentation, thereby fostering the derivation of invaluable insights pivotal to both technical and business-oriented decisions. ITC boasts a plethora of features, from support for a diverse range of technologies and languages, synchronization of data, and customizable templates to reusable libraries and task automation, including data extraction, validation, and document automation. This paper also delves into the predictive analytics aspect of the ITC platform. It demonstrates how ITC harnesses predictive data models, such as Random Forest Regression, to anticipate project outcomes and risks, enhancing decision-making in project management. This feature plays a critical role in the strategic allocation of resources, optimizing project timelines, and promoting overall project success. In an effort to substantiate the efficacy and usability of ITC, we have also incorporated the results and feedback garnered from a comprehensive user assessment conducted in 2022. The feedback suggests promising potential for the platform's application, setting the stage for further development and refinement. The insights provided in this paper not only underline the successful implementation of the ITC platform but also shed light on the transformative impact of predictive analytics in information systems.

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. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

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.

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. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract

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