arXiv subjects

Stephanie Ludi

Publications and source records attributed to Stephanie Ludi.

4 recordsLinked to original sources

arXiv2026

An Extension-Based Accessibility Framework for Making Blockly Accessible to Blind and Low-Vision Users

Block-based programming environments (BBPEs) such as Scratch and Code.org are now widely used in K-12 computer science classes, but they remain mostly inaccessible to blind or visually impaired (BVI) learners. A major problem is that prior accessibility solutions have relied on modifications to the Blockly library, making them difficult to apply in existing BBPEs and thereby limiting adoption. We present an Extension-based Accessibility Framework (EAF) to make BBPEs accessible for BVI students. The framework uses a modular architecture that enables seamless integration with existing Blockly-based BBPEs. We present an innovative three-dimensional (3D) hierarchical navigation model featuring stack labeling and block numbering, mode-based editing to prevent accidental modifications, and WAI-ARIA implementation to ensure compatibility with external screen readers. We evaluated our approach by integrating the EAF framework into two BBPEs (covering 177 test cases) and conducting semi-structured interviews with four participants using VoiceOver, JAWS, and NVDA. Participants reported clearer spatial orientation and easier mental model formation compared to default Blockly keyboard navigation. EAF shows that modular architecture can provide comprehensive accessibility while ensuring compatibility with existing BBPEs.

cs.HC↗

arXiv2023

Do the Test Smells Assertion Roulette and Eager Test Impact Students' Troubleshooting and Debugging Capabilities?

To ensure the quality of a software system, developers perform an activity known as unit testing, where they write code (known as test cases) that verifies the individual software units that make up the system. Like production code, test cases are subject to bad programming practices, known as test smells, that hurt maintenance activities. An essential part of most maintenance activities is program comprehension which involves developers reading the code to understand its behavior to fix issues or update features. In this study, we conduct a controlled experiment with 96 undergraduate computer science students to investigate the impact of two common types of test smells, namely Assertion Roulette and Eager Test, on a student's ability to debug and troubleshoot test case failures. Our findings show that students take longer to correct errors in production code when smells are present in their associated test cases, especially Assertion Roulette. We envision our findings supporting academia in better equipping students with the knowledge and resources in writing and maintaining high-quality test cases. Our experimental materials are available online: https://wajdialjedaani.github.io/testsmellstd/

cs.SE↗

arXiv2021

Test Smell Detection Tools: A Systematic Mapping Study

Test smells are defined as sub-optimal design choices developers make when implementing test cases. Hence, similar to code smells, the research community has produced numerous test smell detection tools to investigate the impact of test smells on the quality and maintenance of test suites. However, little is known about the characteristics, type of smells, target language, and availability of these published tools. In this paper, we provide a detailed catalog of all known, peer-reviewed, test smell detection tools. We start with performing a comprehensive search of peer-reviewed scientific publications to construct a catalog of 22 tools. Then, we perform a comparative analysis to identify the smell types detected by each tool and other salient features that include programming language, testing framework support, detection strategy, and adoption, among others. From our findings, we discover tools that detect test smells in Java, Scala, Smalltalk, and C++ test suites, with Java support favored by most tools. These tools are available as command-line and IDE plugins, among others. Our analysis also shows that most tools overlap in detecting specific smell types, such as General Fixture. Further, we encounter four types of techniques these tools utilize to detect smells. We envision our study as a one-stop source for researchers and practitioners in determining the tool appropriate for their needs. Our findings also empower the community with information to guide future tool development.

cs.SE↗

arXiv2019

Comparison of block-based and hybrid-based environments in transferring programming skills to text-based environments

Teachers face several challenges when presenting the fundamental concepts of programming in the classroom. Several tools are introduced to give a visual dimension to support the learning process. These tools rely on code blocks, easily manipulated in a plug and play fashion, to build a program. These block-based tools intend to familiarize students with programming logic, before diving into text-based programming languages such as Java, Python, etc. However; when transitioning from block-based to text-based programming, students often encounter a gap in their learning. The student may not be able to apply block-based foundations in a text-based environment. To bridge the gap between both environments, we developed a hybrid-based learning approach. We found that on average a hybrid-based approach increases the students understanding of programming foundations, memorization, and ease of transition by more than 30% when compared to a block-based to text-based learning approach. Finally, we provide the community with an open source, hybrid-based learning tool that can be used by students when learning programming concepts or for future studies.

cs.CY↗