Large Language Model-Generated Code (LLMgCode) has become increasingly common in software development. So far LLMgCode has more quality issues than Human-Authored Code (HaCode). It is common for LLMgCode to mix with HaCode in a code change, while the change is signed by only human developers, without being carefully examined. Many automated methods have been proposed to detect LLMgCode from HaCode, in which the perplexity-based method ( Perplexity for short) is the state-of-the-art method. However, the efficacy evaluation of Perplexity has focused on detection accuracy. Yet it is unclear whether Perplexity is good enough in a wider range of realistic evaluation settings. To this end, we carry out a family of experiments to compare Perplexity against feature- and pre-training-based methods from three perspectives: detection accuracy , detection speed , and generalization capability . The experimental results show that Perplexity has the best generalization capability while having limited detection accuracy and detection speed. Based on that, we discuss the strengths and limitations of Perplexity , e.g., Perplexity is unsuitable for high-level programming languages. Finally, we provide recommendations to improve Perplexity and apply it in practice. As the first large-scale investigation on detecting LLMgCode from HaCode, this article provides a wide range of findings for future improvement.

Technology offers new opportunities in healthcare to manage chronic conditions cost-effectively. In the adoption of mHealth services, one of the main barriers is usability, especially for users having limitations or special needs, such as the elderly. Unlike mHealth apps specifically designed for the elderly to monitor and manage chronic conditions such as cardio-vascular health, diabetes, and hypertension, there is a lack of research in pain management, including usability evaluation. Moreover, there is a lack of comprehensive usability evaluation tool. This research focuses on the formulation and assessment of the usability evaluation instrument for the mHealth apps, targeting elderly users. An instrument comprising 8 usability constructs and 58 distinct items was formulated. The content of the instrument was verified by five experts. Face validity was performed with 16 elderly pain sufferers on pre-selected pain apps, it also aids in validating the reliability of the instrument. The research also discusses the mapping of the instrument’s items with the usability measures and elderly barriers influencing technology, with graphical visualization and scores of various artefacts. The graphical details would be helpful in identifying the areas for usability improvement from the elderly’s perspective and the scores would be helpful in comparing usability of different applications.

Testing is a vital activity in software development. The ISO/IEC has defined a standard for system and software quality models called ISO/IEC 25010:2011 to be a guideline and scope for testing any applications. Testing of mobile applications according to this standard, however, is more challenging than other types of software. The diversity of Android devices and various versions of Android operating system, for example, has created a large fragmentation of the Android platform. This fragmentation hinders testing of Android applications especially in relation to portability and compatibility. Existing solutions are either neglecting portability and compatibility issues or lack flexibility in fulfilling needs of the different organizations. We propose a cloud testing model to address the fragmentation of Android platform and provide automated application testing services on the actual devices. The model can be configured in the public, private or hybrid setups to suit individual organizations' needs and budget. A prototype was built based on the model. 10 Android testers used the prototype and the Android Emulator to perform mobile application testing. Results show that the model has the potential to manage the challenging portability and compatibility testing on the Android platform in a flexible and scalable manner.

Over the past 30 years, software developers have been conveniently taking advantage of hardware performance increase, giving little consideration to internal architecture changes of the hardware like central processing unit. In the years to come, these hardware architectural changes will affect software architectures and can no longer be ignored. This is especially true for real-time applications, which tend to push the limits of hardware and take the most advantage of available resources. As a result, computer game applications which are inherently real-time and known for pushing computer hardware boundaries will not be immune. By studying the concepts of concurrency, multithreading and multi-core CPU technology, this paper redefines the existing linear architecture of game engines as a generic concurrent and multi-core friendly architecture. Major game engine modules and their inter-dependencies are identified in order to design the new architecture. A sample game was developed to evaluate the performance of the proposed architecture. The comparison of the test results provided in this paper indicates noticeable improvements in the concurrent architecture over the conventional linear approach.

Interoperability is the ability of systems to provide services to and accept services from other systems, and to use the services exchanged so as to operate together in a more effective manner. The fact that interoperability can be improved means that the metrics for measuring interoperability can be defined. For the purpose of measuring the interoperability between systems, an interoperability assessment model is required. This paper deals with the existing interoperability assessment models. A comparative analysis among these models is provided to evaluate the similarities and differences in their philosophy and implementation. The analysis yields a set of recommendations for any party that is open to the idea of creating or improving an interoperability assessment model.