Abstract
The architectural pattern of micro-services is being increasingly adopted by developers, facilitating the maintenance and scalability of the systems’ code. The adoption and consumption of these micro-services are often seen on the front-end code of the Web applications. Nevertheless, this adoption obliges web designers/developers to know where to look for those web services, to read their documentation and to write the request/response code as well to control the corresponding UI rendering. This whole process is time-consuming and error-prone. This article introduces SeCoGen as an interactive code generator for Web service parsing and consumption. The generator benefits from an HTTP request template, a query normalizer and dynamic UI templates. In order, to validate the generator feasibility and usefulness, a REST API to search for countries is used. © Ricardo Queirós.

Abstract
JavaScript is currently one of the most popular languages worldwide. Its meteoric rise is mainly due to the fact that the language is no longer bound to the limits of the browser and can now be used on several platforms. This growth has led to its increasing use by companies and, consequently, to become part of the curriculum in schools. Meanwhile, in the teaching-learning process of computer programming, teachers continue to use automatic code evaluation systems to relieve their time-consuming and error prone evaluation work. However, these systems reveal a number of issues: they are very generic (one size fits all), they have scarce features to foster exercises authoring, they do not adhere to interoperability standards (e.g. LMS communication), they rely solely on remote evaluators being exposed to single point of failure problems and reducing application performance and user experience, which is a feature well appreciated by the mobile users. In this context, LearnJS is presented as a Web playground for practicing the JavaScript language. The system uses a local evaluator (the user’s own browser) making response times small and thus benefiting the user experience. LearnJS also uses a sophisticated authoring system that allows the teacher to quickly create new exercises and aggregate them into gamified activities. Finally, LearnJS includes universal LMS connectors based on international specifications. In order to validate its use, an evaluation was made by a group of students of Porto Polytechnic aiming to validate the usability of its graphical user interface. © Ricardo Queirós.

Abstract
We are assisting the rise of online coding environments as a strategy to promote youth tech employment. With the growing importance of the technology sector, these type of technical training programs give learners emergent tech skills with a big impact and relevance to the current professional market needs. In this realm, MOOCs (massive open online courses) and online coding bootcamps are two increasingly popular options for learners to improve their code development skills and find work within a relatively short amount of time. Among all the features available on these environments, one stands out, which is the code generation. This chapter aims to detail and compare the most popular solutions for both learning contexts based on several criteria such as impact and maturity, user groups, and tools and features. In the features field, the authors highlight the code generation feature as an efficient way to enhance exercise resolution.

Abstract
In this paper, we identify a subset of types in the rank 2 intersection type system, where types do not contain positive occurrences of intersections. We extend the notion of pre-grammar of a type and address the type-inhabitation problem for types in this subset, as well as their intersections.

Abstract

Abstract
This paper shows that the recent approach to quantitative typing systems for programming languages can be extended to pattern matching features. Indeed, we define two resource-aware type systems, named U and E , for a λ-calculus equipped with pairs for both patterns and terms. Our typing systems borrow some basic ideas from [19], which characterises (head) normalisation in a qualitative way, in the sense that typability and normalisation coincide. But, in contrast to [19], our systems also provide quantitative information about the dynamics of the calculus. Indeed, system U provides upper bounds for the length of (head) normalisation sequences plus the size of their corresponding normal forms, while system E , which can be seen as a refinement of system U , produces exact bounds for each of them. This is achieved by means of a non-idempotent intersection type system equipped with different technical tools. First of all, we use product types to type pairs instead of the disjoint unions in [19], which turn out to be an essential quantitative tool because they remove the confusion between âbeing a pairâ? and âbeing duplicableâ?. Secondly, typing sequents in system E are decorated with tuples of integers, which provide quantitative information about normalisation sequences, notably time (cf. length) and space (cf. size). Moreover, the time resource information is remarkably refined, because it discriminates between different kinds of reduction steps performed during evaluation, so that beta, substitution and matching steps are counted separately. Another key tool of system E is that the type system distinguishes between consuming (contributing to time) and persistent (contributing to space) constructors. © LIPIcs 2020.