We present JS-TOD ( J ava S cript T est O rder-dependency D etector), a tool that can extract, reorder, and rerun Jest tests to reveal possible order-dependent test flakiness. Test order dependency is one of the leading causes of test flakiness. Ideally, each test should operate in isolation and yield consistent results no matter the sequence in which tests are run. However, in practice, test outcomes can vary depending on their execution order. JS-TOD employed a systematic approach to randomising tests, test suites, and describe blocks. The tool is highly customisable, as one can set the number of orders and reruns required (the default setting is 10 reorder and 10 reruns).

Flaky tests pose a significant issue for software testing. A test with a non-deterministic outcome may undermine the reliability of the testing process, making tests untrustworthy. Previous research has identified test order dependency as one of the most prevalent causes of flakiness, particularly in Java and Python. However, little is known about test order dependency in JavaScript tests. This paper aims to investigate test order dependency in JavaScript projects that use Jest, a widely used JavaScript testing framework. We implemented a systematic approach to randomise tests, test suites and describe blocks and produced 10 unique test reorders for each level. We reran each order 10 times (100 reruns for each test suite/project) and recorded any changes in test outcomes. We then manually analysed each case that showed flaky outcomes to determine the cause of flakiness. We examined our detection approach on a dataset of 81 projects obtained from GitHub. Our results revealed 55 order-dependent tests across 10 projects. Most order-dependent tests (52) occurred between tests, while the remaining three occurred between describe blocks. Those order-dependent tests are caused by either shared files (13) or shared mocking state (42) between tests. While sharing files is a known cause of order-dependent tests in other languages, our results underline a new cause (shared mocking state) that was not reported previously.

Passive stylus systems offer a simple and cost-effective solution for digital input, compatible with a wide range of surfaces and devices. This study reviews the domain of passive stylus tracking on passive surfaces, a topic previously underexplored in existing literature. We answer four key research questions: what type of systems exist in this domain, what methods do they use for tracking styli, how accurate are they, and what are their limitations? A systematic literature review resulted in 24 papers describing passive stylus systems. Their methods primarily fall into four categories: monocular cameras with image processing, multiple camera systems with image processing, machine learning systems using high-speed cameras or motion capture hardware, and radio frequency signal-based systems with signal processing. We found the system with the highest accuracy used a single monocular camera. In many systems, markers such as retroreflective spheres, tape, or fiducial markers were used to enhance the feature matching. We have also found stagnation and in some cases, regression in the precision and reliability of these systems over time. The limitations in these systems include the lack of varied stylus form factor support, the restriction to specific camera positions and angles, and the requirement of expensive hardware. Given these findings, we discuss the important characteristics and features of passive stylus systems and propose ways forward in this field.

The Penan people of Malaysian Borneo were traditionally nomads of the rainforest. They would leave messages in the jungle for each other by shaping natural objects into language tokens and arranging these symbols in specific ways -- much like words in a sentence. With settlement, the language is being lost as it is not being used by the younger generation. We report here, a tangible system designed to help the Penan preserve their unique object writing language. The key features of the system are that: its tangibles are made of real objects; it works in the wild; and new tangibles can be fabricated and added to the system by the users. Our evaluations show that the system is engaging and encourages intergenerational knowledge transfer and thus has the potential to help preserve this language.

We present McSig, a multimodal system for teaching blind children cursive handwriting so that they can create a personal signature. For blind people handwriting is very difficult to learn as it is a near-zero feedback activity that is needed only occasionally, yet in important situations; for example, to make an attractive and repeatable signature for legal contracts. McSig aids the teaching of signatures by translating digital ink from the teacher's stylus gestures into three non-visual forms: (1) audio pan and pitch represents the x and y movement of the stylus; (2) kinaesthetic information is provided to the student through a force-feedback haptic pen that mimics the teacher's stylus movement; and (3) a physical tactile line on the writing sheet is created by the haptic pen. McSig has been developed over two major iterations of design, usability testing and evaluation. The final step of the first iteration was a short evaluation with eight visually impaired children. The results suggested that McSig had the highest potential benefit for congenitally and totally blind children and also indicated some areas where McSig could be enhanced. The second prototype incorporated significant modifications to the system, improving the audio, tactile and force-feedback. We then ran a detailed, longitudinal evaluation over 14 weeks with three of the congenitally blind children to assess McSig's effectiveness in teaching the creation of signatures. Results demonstrated the effectiveness of McSig—they all made considerable progress in learning to create a recognizable signature. By the end of ten lessons, two of the children could form a complete, repeatable signature unaided, the third could do so with a little verbal prompting. Furthermore, during this project, we have learnt valuable lessons about providing consistent feedback between different communications channels (by manual interactions, haptic device, pen correction) that will be of interest to others developing multimodal systems.

"McSig" is a multimodal teaching and learning environ-ment for visually-impaired students to learn character shapes, handwriting and signatures collaboratively with their teachers. It combines haptic and audio output to realize the teacher's pen input in parallel non-visual modalities. McSig is intended for teaching visually-impaired children how to handwrite characters (and from that signatures), something that is very difficult without visual feedback. We conducted an evaluation with eight visually-impaired children with a pretest to assess their current skills with a set of character shapes, a training phase using McSig and then a post-test of the same character shapes to see if there were any improvements. The children could all use McSig and we saw significant improvements in the character shapes drawn, particularly by the completely blind children (many of whom could draw almost none of the characters before the test). In particular, the blind participants all expressed enjoyment and excitement about the system and using a computer to learn to handwrite.

The focus of this research is to develop general diagram recognition techniques based on quantitative experiments using machine learning to determine the most significant ink features and effective algorithms for use throughout the recognition process. This should improve on existing recognition success rates.