The old electricity network infrastructure has proven to be inadequate, with respect to modern challenges such as alternative energy sources, electricity demand and energy saving policies. Moreover, ...Information and Communication Technologies (ICT) seem to have reached an adequate level of reliability and flexibility in order to support a new concept of electricity network — the smart grid. In this work, we will analyse the state-of-the-art of smart grids, in their technical, management, security, and optimization aspects. We will also provide a brief overview of the regulatory aspects involved in the development of a smart grid, mainly from the viewpoint of the European Union.
The evaluation and assessment of conversational interfaces is a complex task since such software products are challenging to validate through traditional testing approaches. We conducted a systematic ...Multivocal Literature Review (MLR), on five different literature sources, to provide a view on quality attributes, evaluation frameworks, and evaluation datasets proposed to provide aid to the researchers and practitioners of the field. We came up with a final pool of 118 contributions, including grey (35) and white literature (83). We categorized 123 different quality attributes and metrics under ten different categories and four macro-categories: Relational, Conversational, User-Centered and Quantitative attributes. While Relational and Conversational attributes are most commonly explored by the scientific literature, we testified a predominance of User-Centered Attributes in industrial literature. We also identified five different academic frameworks/tools to automatically compute sets of metrics, and 28 datasets (subdivided into seven different categories based on the type of data contained) that can produce conversations for the evaluation of conversational interfaces. Our analysis of literature highlights that a high number of qualitative and quantitative attributes are available in the literature to evaluate the performance of conversational interfaces. Our categorization can serve as a valid entry point for researchers and practitioners to select the proper functional and non-functional aspects to be evaluated for their products.
Several techniques exist for mobile test automation, from script-based techniques to automated test generation based on GUI models. Most techniques fall short in being adopted extensively by ...practitioners because of the very costly definition (and maintenance) of test cases. We present a novel testing framework for Android apps that allows a developer to write effective test scripts without having to know the implementation details and the user interface of the app under test. The main goal of the framework is to generate adaptive tests that can be executed on a significant number of apps, or different releases of the same app, without manual editing of the tests. The frameworks consists of: (1) a Test Scripting Language, that allows the tester to write generic test scripts tailored to activity and app categories; (2) a State Graph Modeler, that creates a model of the app's GUI, identifying activities (i.e., screens) and widgets; (3) an app classifier that determines the type of application under test; (4) an activity classifier that determines the purpose of each screen; (5) a test adapter that executes test scripts that are compatible with the specific app and activity, automatically tailoring the test scripts to the classes of the app and the activities under test. We evaluated empirically the components of our testing framework. The classifiers were able to outperform available approaches in the literature. The developed testing framework was able to correctly adapt high-level test cases to 28 out of 32 applications, and to reduce the LOCs of the test scripts of around 90%. We conclude that machine learning can be fruitfully applied to the creation of high-level, adaptive test cases for Android apps. Our framework is modular in nature and allows expansions through the addition of new commands to be executed on the classified apps and activities.
Business Process Modeling is a skill that is becoming sought after for computer engineers, with Business Process Modeling Notation (BPMN) being one example of the tools used in modeling activities. ...Students of the Master of Computer Engineering course at Politecnico di Torino learn about BPMN in dedicated courses but often underperform on BPMN-related exercises due to difficulties understanding how to model processes. In recent years, there has been a surge of studies that employ gamification (using game elements in non-recreative contexts to obtain benefits) as a tool in Computer Engineering education to increase students’ engagement with the learning process. This study aims to use the principles of gamification to design a supplementary learning tool for the teaching of information systems technology. In particular, to improve student understanding and use of BPMN diagrams. This study also analyzes the usability and motivation of the participants in using different game elements in increasing student motivation and performance. As part of the study, a prototype web application was developed, which implemented three different designs, each incorporating different game elements relating to either progress, competition, or rewards. An evaluation was then conducted on the prototype to evaluate the performance of the practitioners in performing BPMN modeling tasks with the gamified tool, the usability of the proposed mechanics and the enjoyment of the individual game mechanics that were implemented. With the usage of the gamified tool, the users of the experimental sample were able to complete BPMN modeling tasks with performances compatible with estimates made through expert judgement (i.e., gamification had no negative effect on performance), and were motivated to check the correctness of their models many times during the task execution. The system was evaluated as highly usable (85.8 System Usability Score); the most enjoyed game elements were rewards, levels, progress bars and aesthetics.
Rust is an innovative programming language initially implemented by Mozilla, developed to ensure high performance, reliability, and productivity. The final purpose of this study consists of applying ...a set of common static software metrics to programs written in Rust to assess the verbosity, understandability, organization, complexity, and maintainability of the language. To that extent, nine different implementations of algorithms available in different languages were selected. We computed a set of metrics for Rust, comparing them with the ones obtained from C and a set of object-oriented languages: C++, Python, JavaScript, TypeScript. To parse the software artifacts and compute the metrics, it was leveraged a tool called
that was extended with a software module, written in Python, with the aim of uniforming and comparing the results. The Rust code had an average verbosity in terms of the raw size of the code. It exposed the most structured source organization in terms of the number of methods. Rust code had a better Cyclomatic Complexity, Halstead Metrics, and Maintainability Indexes than C and C++ but performed worse than the other considered object-oriented languages. Lastly, the Rust code exhibited the lowest COGNITIVE complexity of all languages. The collected measures prove that the Rust language has average complexity and maintainability compared to a set of popular languages. It is more easily maintainable and less complex than the C and C++ languages, which can be considered syntactically similar. These results, paired with the memory safety and safe concurrency characteristics of the language, can encourage wider adoption of the language of Rust in substitution of the C language in both the open-source and industrial environments.
Energy efficiency is an increasingly important non-functional property of software, especially when it runs on mobile or IoT devices. An engineering approach demands a reliable measurement of energy ...consumption of software while performing computational tasks. In this paper, we describe PowTrAn, an R package supporting the analysis of the power traces of a device executing software tasks. The tool analyzes traces with embedded markers, a non-invasive technique that enables gauging software efficiency based on the energy consumed by the whole device. The package effectively handles large power traces, detects work units, and computes correct energy measures, even in noisy conditions, such as those caused by multiple processes working simultaneously. PowTrAn was validated on applications in realistic conditions and multiple hardware configurations. PowTrAn also provides data visualization that helps the user to assess the measurement consistency, and it also helps to highlight possible energy outliers.
The literature proposes many software metrics for evaluating the source code non-functional properties, such as its complexity and maintainability. The literature also proposes several tools to ...compute those properties on source codes developed with many different software languages. However, the Rust language emergence has not been paired by the community’s effort in developing parsers and tools able to compute metrics for the Rust source code. Also, metrics tools often fall short in providing immediate means of comparing maintainability metrics between different algorithms or coding languages. We hence introduce rust-code-analysis, a Rust library that allows the extraction of a set of eleven maintainability metrics for ten different languages, including Rust. rust-code-analysis, through the Abstract Syntax Tree (AST) of a source file, allows the inspection of the code structure, analyzing source code metrics at different levels of granularity, and finding code syntax errors before compiling time. The tool also offers a command-line interface that allows exporting the results in different formats. The possibility of analyzing source codes written in different programming languages enables simple and systematic comparisons between the metrics produced from different empirical and large-scale analysis sources.
Gamification is an established practice in Software Engineering to increase effectiveness and engagement in many practices. This manuscript provides a characterization of the application of ...gamification to the Software Testing area. Such practice in fact reportedly suffers from low engagement by both personnel in industrial contexts and learners in educational contexts. Our goal is to identify the application areas and utilized gamified techniques and mechanics, the provided benefits and drawbacks, as well as the open challenges in the field. To this purpose, we conducted a Multivocal Literature Review to identify white and grey literature sources addressing gamified software testing.We analyzed 73 contributions and summarized the most common gamified mechanics, concepts, tools, and domains where they are mostly applied. We conclude that gamification in software testing is mostly applied to the test creation phase with simple white-box unit or mutation testing tools and is mostly used to foster good behaviors by promoting the testers’ accomplishment. Key research areas and main challenges in the field are: careful design of tailored gamified mechanics for specific testing techniques; the need for technological improvements to enable crowdsourcing, cooperation, and concurrency; the necessity for empirical and large-scale evaluation of the benefits delivered by gamification mechanics.
Online non-intrusive load monitoring algorithms have captivated academia and industries as parsimonious solutions for household energy efficiency monitoring as well as a safety control, anomaly ...detection, and demand-side management. However, the computational energy cost for executing such algorithms should not overcome the promised energy efficiency from the disaggregated appliance specific consumption information feed-backs. Moreover, the energy efficiency of cloud computing systems is also becoming a concern for the environment due to carbon emission. This study analyzes the energy spent to execute NILM algorithms via computation cost estimation and prediction using computing system-level power monitoring and data-driven approaches. A generic framework for an automated algorithm cost monitoring and modeling methodologies is devised for large load scale deployment of Cloud-based Online-NILM algorithms. The efficacy of the proposed approach was examined and validated on two computing system use-cases, i.e., Dedicated Server and Cloud Virtual Server. The prediction models, developed using statistical and machine learning tools, demonstrate the promising applicability of the data-driven approach with a very high prediction accuracy without detailed knowledge of the computing systems and the algorithm.
Context:Kotlin is a new programming language representing an alternative to Java; they both target the same JVM and can safely coexist in the same application. Kotlin is advertised as capable to ...solve several known limitations of Java. Recent surveys show that Kotlin achieved a relevant diffusion among Java developers. Goal:We planned to empirically assess a few typical promises of Kotlin w.r.t. known Java’s limitations, in terms of development effectiveness, maintainability, and ease of development. Method:Our experiment involved 27 teams of 4 people each that completed a set of maintenance tasks (both defect correction and feature addition) on Android apps written in either Java or Kotlin. In addition to the number of fixed defects, effort, and code size, we collected, though a questionnaire, the participants’ perceptions about the avoidance of known pitfalls. Results:We did not observe any significant difference in terms of maintainability between the two languages.We found a significant difference regarding the amount of code written, which constitutes evidence of better conciseness of Kotlin. Concerning ease of development, the frequency of NullPointerExceptions reported by the subjects was significantly lower when developing in Kotlin. On the other hand, no significant difference was found in the occurrence of other common Java pitfalls. Finally, the IDE support was deemed better for Java than Kotlin. Conclusions:Some of the promises of Kotlin to be a ”better Java” have been confirmed by our empirical assessment. Evidence suggests that the effort in transitioning to Kotlin can provide some advantages to Java developers, especially regarding code conciseness.Our results may serve as the basis for further investigations on the properties of the language.
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