We study how students and teachers conceptualize the notion of 'algorithm', a fundamental concept in computer science and computer science curricula. We analyze the work produced by CS students and teachers during a workshop conducted repeatedly over several years in some outreach activities for schools, computing education courses, and professional development opportunities for teachers. Participants were divided into groups, given some procedures written in natural language, and asked to decide together which of the procedures might be taken as algorithms. The procedures were purposely designed to present flaws or features that could activate discussion. After that, groups were asked to agree upon a definition of 'algorithm' and make its fundamental properties explicit. The activity triggered reflections around the idea of algorithm and its interpreter, going beyond stereotyped definitions, and leading participants to deepen their comprehension of the notion. We report on the aspects that were more debated by the groups, and those that were recurrent in the resulting definitions. We argue that this kind of activities should be offered more often both to students during their study career, and to teachers in professional development opportunities, to prompt them to reflect on computing foundations also in a non-technical, more holistic way.

In this paper we study the problem-solving ability of the Large Language Model known as GPT-3 (codename DaVinci), by considering its performance in solving tasks proposed in the “Bebras International Challenge on Informatics and Computational Thinking”. In our experiment, GPT-3 was able to answer with a majority of correct answers about one third of the Bebras tasks we submitted to it. The linguistic fluency of GPT-3 is impressive and, at a first reading, its explanations sound coherent, on-topic and authoritative; however the answers it produced are in fact erratic and the explanations often questionable or plainly wrong. The tasks in which the system performs better are those that describe a procedure, asking to execute it on a specific instance of the problem. Tasks solvable with simple, one-step deductive reasoning are more likely to obtain better answers and explanations. Synthesis tasks, or tasks that require a more complex logical consistency get the most incorrect answers.

The Bebras International Challenge on Informatics and Computational Thinking is a contest open to pupils of all school levels (from primary up to upper secondary) based on tasks rooted on core informatics concepts, yet independent of specific previous knowledge such as for instance that acquired during curricular activities.This paper describes the design choices, the architecture, and the main features of the web-based platform used to carry out the Italian Bebras contest.This platform includes functionalities needed by students, teachers, and Bebras staff during the execution of the challenge, tools to support the preparation of tasks and the training of students, instruments to evaluate the results and analyse data collected during the challenge.The platform is online since 2015 and it has managed the participation of around 25,000 teams and a significant amount of training sessions.

Modern software systems are required to operate in a highly uncertain and changing environment. They have to control the satisfaction of their requirements at run-time, and possibly adapt and cope with situations that have not been completely addressed at design-time. Software engineering methods and techniques are, more than ever, forced to deal with change and uncertainty (lack of knowledge) explicitly. For tackling the challenge posed by uncertainty in delivering more reliable systems, this paper proposes a novel online Model-based Testing technique that complements classic test case generation based on pseudo-random sampling strategies with an uncertainty-aware sampling strategy. To deal with system uncertainty during testing, the proposed strategy builds on an Inverse Uncertainty Quantification approach that is related to the discrepancy between the measured data at run-time (while the system executes) and a Markov Decision Process model describing the behavior of the system under test. To this purpose, a conformance game approach is adopted in which tests feed a Bayesian inference calibrator that continuously learns from test data to tune the system model and the system itself. A comparative evaluation between the proposed uncertainty-aware sampling policy and classical pseudo-random sampling policies is also presented using the Tele Assistance System running example, showing the differences in achieved accuracy and efficiency.

Summary The recent extensive availability of ‘cloud’ computing platforms is very appealing for the formal verification community. In fact, these platforms represent a great opportunity to run massively parallel jobs and analyze ‘big data’ problems, although classical formal verification tools and techniques must undergo a deep technological transformation to take advantage of the available powerful architectures. A distributed approach to verification of computation tree logic formulas on very large state spaces is described. The approach exploits and integrates our parametric state–space builder, designed to ease the adoption of ‘big data’ platforms. The whole framework adopts aMAPREDUCEapproach as the core computational model and can be tailored to different modeling formalisms. This paper includes proofs of correctness, a short theoretical discussion about complexity, and reports a practical experience with some benchmarking Petri net models. The outcomes of several tests are presented, thus showing the convenience of the proposed approach. Copyright © 2015 John Wiley & Sons, Ltd.

This paper analyses the results of the 2014 edition of the Italian Bebras/Kangourou contest, exploiting the Item Response Theory statistical methodology in order to infer the difficulty of each of the proposed tasks starting from the scores attained by the participants. Such kind of analysis, enabling the organizers of the contest to check whether or not the difficulty perceived by pupils was substantially different from that estimated by those who proposed the tasks, is important as a feedback in order to gain knowledge to be used both in ranking participants and in organizing future editions of the contest. We show how the proposed analysis essentially highlights that the 63% of tasks was perceived at the same level of difficulty estimated by those who proposed them, but a 37% of tasks were either easier or more difficult than expected.

This article describes the state of informatics education in the Italian secondary schools, highlighting how the learning objectives set up by the Ministry of Education are difficult to meet, due to the fact that the subject is often taught by teachers not holding an informatics degree, the lack of suitable teaching material and the expectations of pupils and families, who tend to identify informatics with the use of computer applications.

In this paper we present the difficulties involved in recognizing pitch-shifted audio, discussing its usage and implications. In the framework of audio fingerprinting techniques, we address the issue by proposing two complementary solutions, both of which can be exploited without affecting an existing reference database. As fingerprint algorithm, a well-known, robust method is employed in a modified version and providing insights on its peculiarities. Tests were carried out on a vast song library and exhibit an excellent success rate, up to considerable distortion magnitudes.

In this paper we deal with the problem of querying, navigating and exchanging UML design artefacts, including components interfaces, conceptual schemas and the like. Our approach relies on a hierarchy of XML-based descriptors for UML components, representing both functional and non-functional features of artefacts. After presenting a “query-friendly” DTD for such descriptors, the paper illustrates issues related to querying and browsing design repositories. We show how our approach can also address connection-oriented services, such as HTTP-based Web sites.

This article presents a hierarchy-aware classification schema for obje ct-oriented code, where software components are classified according to their behavioral characteristics , such as provided services, employed algorithms, and needed data. In the case of reusable application frameworks, these characteristics are constructured from their model , i.e., from the description of the abstract classes specifying both the framework structure and purpose. In conventional object libraries, the characteristics are extracted semiautomatically from class interfaces. Characteristics are term pairs, weighted to represent “how well” they describe component behavior. The set of characteristics associated with a given component forms its software descriptor . A descriptor base is presented where descriptors are organized on the basis of structured relationships, such as similarity and composition. The classification is supported by a thesaurus acting as a language-independent unified lexicon. The descriptor base is conceived for developers who, besides conventionally browsing the descriptors hierarchy, can query the system, specifying a set of desired functionalities and getting a ranked set of adaptable candidates. User feedback is taken into account in order to progressively ameliorate the quality of the descriptors according to the views of the user community. Feedback is made dependent of the user typology through a user profile . Experimental results in terms of recall and precision of the retrieval mechanism against a sample code base are reported.

This article presents a hierarchy-aware classification schema for object-oriented code, where software components are classified according to their behavioral characteristics , such as provided services, employed algorithms, and needed data. In the case of reusable application frameworks, these characteristics are constructed from their model , i.e., from the description of the abstract classes specifying both the framework structure and purpose. In conventional object libraries, the characteristics are extracted semiautomatically from class interfaces. Characteristics are term pairs, weighted to represent "how well" they describe component behavior. The set of characteristics associated with a given component forms its software descriptor . A descriptor base is presented where descriptors are organized on the basis of structured relationships, such as similarity and composition. The classification is supported by a thesaurus acting as a language-independent unified lexicon. The descriptor base is conceived for developers who, besides conventionally browsing the descriptors hierarchy, can query the system, specifying a set of desired functionalities and getting a ranked set of adaptable candidates. User feedback is taken into account in order to progressively ameliorate the quality of the descriptors according to the views of the user community. Feedback is made dependent of the user typology through a user profile . Experimental results in terms of recall and precision of the retrieval mechanism against a sample code base are reported.