Designing a Programming Game to Improve Children’s Procedural Abstraction Skills in Scratch

© The Author(s) 2020. The recent shift in compulsory education from ICT-focused computing curricula to informatics, digital literacy and computer science, has resulted in children being taught computing using block-based programming tools such as Scratch, with teaching that is often limited by school resources and teacher expertise. Even without these limitations, Scratch users often produce code with ‘code smells’ such as duplicate blocks and long scripts which impact how they understand and debug projects. These code smells can be removed using procedural abstraction, an important concept in computer science rarely taught to this age group. This article describes the design of a novel educational block-based programming game, Pirate Plunder, which concentrates on how procedural abstraction is introduced and reinforced. The article then reports an extended evaluation to measure the game’s efficacy with children aged 10 and 11, finding that children who played the game were then able to use procedural abstraction in Scratch. The article then uses game analytics to explore why the game was effective and gives three recommendations for educational game design based on this research: using learning trajectories and restrictive success conditions to introduce complex content, increasing learner investment through customisable avatars and suggestions for improving the evaluations of educational games

DRAFT-What you always wanted to know but could not find about block-based environments

Block-based environments are visual programming environments, which are becoming more and more popular because of their ease of use. The ease of use comes thanks to their intuitive graphical representation and structural metaphors (jigsaw-like puzzles) to display valid combinations of language constructs to the users. Part of the current popularity of block-based environments is thanks to Scratch. As a result they are often associated with tools for children or young learners. However, it is unclear how these types of programming environments are developed and used in general. So we conducted a systematic literature review on block-based environments by studying 152 papers published between 2014 and 2020, and a non-systematic tool review of 32 block-based environments. In particular, we provide a helpful inventory of block-based editors for end-users on different topics and domains. Likewise, we focused on identifying the main components of block-based environments, how they are engineered, and how they are used. This survey should be equally helpful for language engineering researchers and language engineers alike

Understanding and Mitigating Flaky Software Test Cases

A flaky test is a test case that can pass or fail without changes to the test case code or the code under test. They are a wide-spread problem with serious consequences for developers and researchers alike. For developers, flaky tests lead to time wasted debugging spurious failures, tempting them to ignore future failures. While unreliable, flaky tests can still indicate genuine issues in the code under test, so ignoring them can lead to bugs being missed. The non-deterministic behaviour of flaky tests is also a major snag to continuous integration, where a single flaky test can fail an entire build. For researchers, flaky tests challenge the assumption that a test failure implies a bug, an assumption that many fundamental techniques in software engineering research rely upon, including test acceleration, mutation testing, and fault localisation. Despite increasing research interest in the topic, open problems remain. In particular, there has been relatively little attention paid to the views and experiences of developers, despite a considerable body of empirical work. This is essential to guide the focus of research into areas that are most likely to be beneficial to the software engineering industry. Furthermore, previous automated techniques for detecting flaky tests are typically either based on exhaustively rerunning test cases or machine learning classifiers. The prohibitive runtime of the rerunning approach and the demonstrably poor inter-project generalisability of classifiers leaves practitioners with a stark choice when it comes to automatically detecting flaky tests. In response to these challenges, I set two high-level goals for this thesis: (1) to enhance the understanding of the manifestation, causes, and impacts of flaky tests; and (2) to develop and empirically evaluate efficient automated techniques for mitigating flaky tests. In pursuit of these goals, this thesis makes five contributions: (1) a comprehensive systematic literature review of 76 published papers; (2) a literature-guided survey of 170 professional software developers; (3) a new feature set for encoding test cases in machine learning-based flaky test detection; (4) a novel approach for reducing the time cost of rerunning-based techniques for detecting flaky tests by combining them with machine learning classifiers; and (5) an automated technique that detects and classifies existing flaky tests in a project and produces reusable project-specific machine learning classifiers able to provide fast and accurate predictions for future test cases in that project