arXiv subjects

Gordon Fraser

Publications and source records attributed to Gordon Fraser.

At least 73 records · Page 4Linked to original sources

arXiv2022

An Empirical Study of Automated Unit Test Generation for Python

Various mature automated test generation tools exist for statically typed programming languages such as Java. Automatically generating unit tests for dynamically typed programming languages such as Python, however, is substantially more difficult due to the dynamic nature of these languages as well as the lack of type information. Our Pynguin framework provides automated unit test generation for Python. In this paper, we extend our previous work on Pynguin to support more aspects of the Python language, and by studying a larger variety of well-established state of the art test-generation algorithms, namely DynaMOSA, MIO, and MOSA. Furthermore, we improved our Pynguin tool to generate regression assertions, whose quality we also evaluate. Our experiments confirm that evolutionary algorithms can outperform random test generation also in the context of Python, and similar to the Java world, DynaMOSA yields the highest coverage results. However, our results also demonstrate that there are still fundamental remaining issues, such as inferring type information for code without this information, currently limiting the effectiveness of test generation for Python.

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arXiv2022

Scratch as Social Network: Topic Modeling and Sentiment Analysis in Scratch Projects

Societal matters like the Black Lives Matter (BLM) movement influence software engineering, as the recent debate on replacing certain discriminatory terms such as whitelist/blacklist has shown. Identifying relevant and trending societal matters is important, and often done using social network analysis for traditional social media channels such as twitter. In this paper we explore whether this type of analysis can also be used for introspection of the software world, by looking at the thriving scene of young Scratch programmers. The educational programming language Scratch is not only used for teaching programming concepts, but offers a platform for young programmers to express and share their creativity on any topics of relevance. By analyzing titles and project comments in a dataset of 106.032 Scratch projects, we explore which topics are common in the Scratch community, whether socially relevant events are reflected and how how the sentiment in the comments is. It turns out that the diversity of topics within the Scratch projects make the analysis process challenging. Our results nevertheless show that topics from pop and net culture in particular are present, and even recent societal events such as the Covid-19 pandemic or BLM are to some extent reflected in Scratch. The tone in the comments is mostly positive with catchy youth language. Hence, despite the challenges, Scratch projects can be studied in the same way as social networks, which opens up new possibilities to improve our understanding of the behavior and motivation of novice programmers.

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arXiv2022

A Survey on How Test Flakiness Affects Developers and What Support They Need To Address It

Non-deterministically passing and failing test cases, so-called flaky tests, have recently become a focus area of software engineering research. While this research focus has been met with some enthusiastic endorsement from industry, prior work nevertheless mostly studied flakiness using a code-centric approach by mining software repositories. What data extracted from software repositories cannot tell us, however, is how developers perceive flakiness: How prevalent is test flakiness in developers' daily routine, how does it affect them, and most importantly: What do they want us researchers to do about it? To answer these questions, we surveyed 335 professional software developers and testers in different domains. The survey respondents confirm that flaky tests are a common and serious problem, thus reinforcing ongoing research on flaky test detection. Developers are less worried about the computational costs caused by re-running tests and more about the loss of trust in the test outcomes. Therefore, they would like to have IDE plugins to detect flaky code as well as better visualizations of the problem, particularly dashboards showing test outcomes over time; they also wish for more training and information on flakiness. These important aspects will require the attention of researchers as well as tool developers.

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arXiv2022

Gamekins: Gamifying Software Testing in Jenkins

Developers have to write thorough tests for their software in order to find bugs and to prevent regressions. Writing tests, however, is not every developer's favourite occupation, and if a lack of motivation leads to a lack of tests, then this may have dire consequences, such as programs with poor quality or even project failures. This paper introduces Gamekins, a tool that uses gamification to motivate developers to write more and better tests. Gamekins is integrated into the Jenkins continuous integration platform where game elements are based on commits to the source code repository: Developers can earn points for completing test challenges and quests posed by Gamekins, compete with other developers or developer teams on a leaderboard, and are rewarded for their test-related achievements.

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arXiv2022

Model-based Testing of Scratch Programs

Learners are often introduced to programming via dedicated languages such as Scratch, where block-based commands are assembled visually in order to control the interactions of graphical sprites. Automated testing of such programs is an important prerequisite for supporting debugging, providing hints, or assessing learning outcomes. However, writing tests for Scratch programs can be challenging: The game-like and randomised nature of typical Scratch programs makes it difficult to identify specific timed input sequences used to control the programs. Furthermore, precise test assertions to check the resulting program states are incompatible with the fundamental principle of creative freedom in programming in Scratch, where correct program behaviour may be implemented with deviations in the graphical appearance or timing of the program. The event-driven and actor-oriented nature of Scratch programs, however, makes them a natural fit for describing program behaviour using finite state machines. In this paper, we introduce a model-based testing approach by extending Whisker, an automated testing framework for Scratch programs. The model-based extension describes expected program behaviour in terms of state machines, which makes it feasible to check the abstract behaviour of a program independent of exact timing and pixel-precise graphical details, and to automatically derive test inputs testing even challenging programs. A video demonstrating model-based testing with Whisker is available at the following URL: https://youtu.be/edgCNbGSGEY

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arXiv2022

Pynguin: Automated Unit Test Generation for Python

Automated unit test generation is a well-known methodology aiming to reduce the developers' effort of writing tests manually. Prior research focused mainly on statically typed programming languages like Java. In practice, however, dynamically typed languages have received a huge gain in popularity over the last decade. This introduces the need for tools and research on test generation for these languages, too. We introduce Pynguin, an extendable test-generation framework for Python, which generates regression tests with high code coverage. Pynguin is designed to be easily usable by practitioners; it is also extensible to allow researchers to adapt it for their needs and to enable future research. We provide a demo of Pynguin at https://youtu.be/UiGrG25Vts0; further information, documentation, the tool, and its source code are available at https://www.pynguin.eu.

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arXiv2021

Common Bugs in Scratch Programs

Bugs in Scratch programs can spoil the fun and inhibit learning success. Many common bugs are the result of recurring patterns of bad code. In this paper we present a collection of common code patterns that typically hint at bugs in Scratch programs, and the LitterBox tool which can automatically detect them. We empirically evaluate how frequently these patterns occur, and how severe their consequences usually are. While fixing bugs inevitably is part of learning, the possibility to identify the bugs automatically provides the potential to support learners

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arXiv2021

Challenging but Full of Opportunities: Teachers' Perspectives on Programming in Primary Schools

The widespread establishment of computational thinking in school curricula requires teachers to introduce children to programming already at primary school level. As this is a recent development, primary school teachers may neither be adequately prepared for how to best teach programming, nor may they be fully aware why they have to do so. In order to gain a better understanding of these questions, we contrast insights taken from practical experiences with the anticipations of teachers in training. By surveying 200 teachers who have taught programming at primary schools and 97 teachers in training, we identify relevant challenges when teaching programming, opportunities that arise when children learn programming, and strategies how to address both of these in practice. While many challenges and opportunities are correctly anticipated, we find several disagreements that can inform revisions of the curricula in teaching studies to better prepare primary school teachers for teaching programming at primary schools.

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arXiv2021

Improving Readability of Scratch Programs with Search-based Refactoring

Block-based programming languages like Scratch have become increasingly popular as introductory languages for novices. These languages are intended to be used with a "tinkering" approach which allows learners and teachers to quickly assemble working programs and games, but this often leads to low code quality. Such code can be hard to comprehend, changing it is error-prone, and learners may struggle and lose interest. The general solution to improve code quality is to refactor the code. However, Scratch lacks many of the common abstraction mechanisms used when refactoring programs written in higher programming languages. In order to improve Scratch code, we therefore propose a set of atomic code transformations to optimise readability by (1) rewriting control structures and (2) simplifying scripts using the inherently concurrent nature of Scratch programs. By automating these transformations it is possible to explore the space of possible variations of Scratch programs. In this paper, we describe a multi-objective search-based approach that determines sequences of code transformations which improve the readability of a given Scratch program and therefore form refactorings. Evaluation on a random sample of 1000 Scratch programs demonstrates that the generated refactorings reduce complexity and entropy in 70.4% of the cases, and 354 projects are improved in at least one metric without making any other metric worse. The refactored programs can help both novices and their teachers to improve their code.

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arXiv2021

Effects of Hints on Debugging Scratch Programs: An Empirical Study with Primary School Teachers in Training

Bugs in learners' programs are often the result of fundamental misconceptions. Teachers frequently face the challenge of first having to understand such bugs, and then suggest ways to fix them. In order to enable teachers to do so effectively and efficiently, it is desirable to support them in recognising and fixing bugs. Misconceptions often lead to recurring patterns of similar bugs, enabling automated tools to provide this support in terms of hints on occurrences of common bug patterns. In this paper, we investigate to what extent the hints improve the effectiveness and efficiency of teachers in debugging learners' programs using a cohort of 163 primary school teachers in training, tasked to correct buggy Scratch programs, with and without hints on bug patterns. Our experiment suggests that automatically generated hints can reduce the effort of finding and fixing bugs from 8.66 to 5.24 minutes, while increasing the effectiveness by 34% more correct solutions. While this improvement is convincing, arguably teachers in training might first need to learn debugging "the hard way" to not miss the opportunity to learn by relying on tools. We therefore investigate whether the use of hints during training affects their ability to recognise and fix bugs without hints. Our experiment provides no significant evidence that either learning to debug with hints or learning to debug "the hard way" leads to better learning effects. Overall, this suggests that bug patterns might be a useful concept to include in the curriculum for teachers in training, while tool-support to recognise these patterns is desirable for teachers in practice.

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arXiv2021

Data-driven Analysis of Gender Differences and Similarities in Scratch Programs

Block-based programming environments such as Scratch are an essential entry point to computer science. In order to create an effective learning environment that has the potential to address the gender imbalance in computer science, it is essential to better understand gender-specific differences in how children use such programming environments. In this paper, we explore gender differences and similarities in Scratch programs along two dimensions: In order to understand what motivates girls and boys to use Scratch, we apply a topic analysis using unsupervised machine learning for the first time on Scratch programs, using a dataset of 317 programs created by girls and boys in the range of 8-10 years. In order to understand how they program for these topics, we apply automated program analysis on the code implemented in these projects. We find that, in-line with common stereotypes, girls prefer topics that revolve around unicorns, celebrating, dancing and music, while boys tend to prefer gloomy topics with bats and ghouls, or competitive ones such as soccer or basketball. Girls prefer animations and stories, resulting in simpler control structures, while boys create games with more loops and conditional statements, resulting in more complex programs. Considering these differences can help to improve the learning outcomes and the resulting computing-related self-concepts, which are prerequisites for developing a longer-term interest in computer science.

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arXiv2021

An Experience of Introducing Primary School Children to Programming using Ozobots (Practical Report)

Algorithmic thinking is a central concept in the context of computational thinking, and it is commonly taught by computer programming. A recent trend is to introduce basic programming concepts already very early on at primary school level. There are, however, several challenges in teaching programming at this level: Schools and teachers are often neither equipped nor trained appropriately, and the best way to move from initial "unplugged" activities to creating programs on a computer are still a matter of open debate. In this paper, we describe our experience of a small INTERREG-project aiming at supporting local primary schools in introducing children to programming concepts using Ozobot robots. These robots have two distinct advantages: First, they can be programmed with and without computers, thus helping the transition from unplugged programming to programming with a computer. Second, they are small and easy to transport, even when used together with tablet computers. Although we learned in our outreach events that the use of Ozobots is not without challenges, our overall experience is positive and can hopefully support others in setting up first encounters with programming at primary schools.

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arXiv2021

Code Perfumes: Reporting Good Code to Encourage Learners

Block-based programming languages like Scratch enable children to be creative while learning to program. Even though the block-based approach simplifies the creation of programs, learning to program can nevertheless be challenging. Automated tools such as linters therefore support learners by providing feedback about potential bugs or code smells in their programs. Even when this feedback is elaborate and constructive, it still represents purely negative criticism and by construction ignores what learners have done correctly in their programs. In this paper we introduce an orthogonal approach to linting: We complement the criticism produced by a linter with positive feedback. We introduce the concept of code perfumes as the counterpart to code smells, indicating the correct application of programming practices considered to be good. By analysing not only what learners did wrong but also what they did right we hope to encourage learners, to provide teachers and students a better understanding of learners' progress, and to support the adoption of automated feedback tools. Using a catalogue of 25 code perfumes for Scratch, we empirically demonstrate that these represent frequent practices in Scratch, and we find that better programs indeed contain more code perfumes.

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arXiv2021

Guiding Next-Step Hint Generation Using Automated Tests

Learning basic programming with Scratch can be hard for novices and tutors alike: Students may not know how to advance when solving a task, teachers may face classrooms with many raised hands at a time, and the problem is exacerbated when novices are on their own in online or virtual lessons. It is therefore desirable to generate next-step hints automatically to provide individual feedback for students who are stuck, but current approaches rely on the availability of multiple hand-crafted or hand-selected sample solutions from which to draw valid hints, and have not been adapted for Scratch. Automated testing provides an opportunity to automatically select suitable candidate solutions for hint generation, even from a pool of student solutions using different solution approaches and varying in quality. In this paper we present Catnip, the first next-step hint generation approach for Scratch, which extends existing data-driven hint generation approaches with automated testing. Evaluation of Catnip on a dataset of student Scratch programs demonstrates that the generated hints point towards functional improvements, and the use of automated tests allows the hints to be better individualized for the chosen solution path.

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arXiv2021

SnapCheck: Automated Testing for Snap Programs

Programming environments such as Snap, Scratch, and Processing engage learners by allowing them to create programming artifacts such as apps and games, with visual and interactive output. Learning programming with such a media-focused context has been shown to increase retention and success rate. However, assessing these visual, interactive projects requires time and laborious manual effort, and it is therefore difficult to offer automated or real-time feedback to students as they work. In this paper, we introduce SnapCheck, a dynamic testing framework for Snap that enables instructors to author test cases with Condition-Action templates. The goal of SnapCheck is to allow instructors or researchers to author property-based test cases that can automatically assess students' interactive programs with high accuracy. Our evaluation of SnapCheck on 162 code snapshots from a Pong game assignment in an introductory programming course shows that our automated testing framework achieves at least 98% accuracy over all rubric items, showing potentials to use SnapCheck for auto-grading and providing formative feedback to students.

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arXiv2021

Does mutation testing improve testing practices?

Various proxy metrics for test quality have been defined in order to guide developers when writing tests. Code coverage is particularly well established in practice, even though the question of how coverage relates to test quality is a matter of ongoing debate. Mutation testing offers a promising alternative: Artificial defects can identify holes in a test suite, and thus provide concrete suggestions for additional tests. Despite the obvious advantages of mutation testing, it is not yet well established in practice. Until recently, mutation testing tools and techniques simply did not scale to complex systems. Although they now do scale, a remaining obstacle is lack of evidence that writing tests for mutants actually improves test quality. In this paper we aim to fill this gap: By analyzing a large dataset of almost 15 million mutants, we investigate how these mutants influenced developers over time, and how these mutants relate to real faults. Our analyses suggest that developers using mutation testing write more tests, and actively improve their test suites with high quality tests such that fewer mutants remain. By analyzing a dataset of past fixes of real high-priority faults, our analyses further provide evidence that mutants are indeed coupled with real faults. In other words, had mutation testing been used for the changes introducing the faults, it would have reported a live mutant that could have prevented the bug.

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arXiv2021

Practical Mutation Testing at Scale

Mutation analysis assesses a test suite's adequacy by measuring its ability to detect small artificial faults, systematically seeded into the tested program. Mutation analysis is considered one of the strongest test-adequacy criteria. Mutation testing builds on top of mutation analysis and is a testing technique that uses mutants as test goals to create or improve a test suite. Mutation testing has long been considered intractable because the sheer number of mutants that can be created represents an insurmountable problem -- both in terms of human and computational effort. This has hindered the adoption of mutation testing as an industry standard. For example, Google has a codebase of two billion lines of code and more than 500,000,000 tests are executed on a daily basis. The traditional approach to mutation testing does not scale to such an environment. To address these challenges, this paper presents a scalable approach to mutation testing based on the following main ideas: (1) Mutation testing is done incrementally, mutating only changed code during code review, rather than the entire code base; (2) Mutants are filtered, removing mutants that are likely to be irrelevant to developers, and limiting the number of mutants per line and per code review process; (3) Mutants are selected based on the historical performance of mutation operators, further eliminating irrelevant mutants and improving mutant quality. Evaluation in a code-review-based setting with more than 24,000 developers on more than 1,000 projects shows that the proposed approach produces orders of magnitude fewer mutants and that context-based mutant filtering and selection improve mutant quality and actionability. Overall, the proposed approach represents a mutation testing framework that seamlessly integrates into the software development workflow and is applicable up to large-scale industrial settings.

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arXiv2021

LitterBox: A Linter for Scratch Programs

Creating programs with block-based programming languages like Scratch is easy and fun. Block-based programs can nevertheless contain bugs, in particular when learners have misconceptions about programming. Even when they do not, Scratch code is often of low quality and contains code smells, further inhibiting understanding, reuse, and fun. To address this problem, in this paper we introduce LitterBox, a linter for Scratch programs. Given a program or its public project ID, LitterBox checks the program against patterns of known bugs and code smells. For each issue identified, LitterBox provides not only the location in the code, but also a helpful explanation of the underlying reason and possible misconceptions. Learners can access LitterBox through an easy to use web interface with visual information about the errors in the block-code, while for researchers LitterBox provides a general, open source, and extensible framework for static analysis of Scratch programs.

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