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In software development, identifying software faults is an important task. The presence of faults not only reduces the quality of the software, but also increases the cost of development life cycle. Fault identification can be performed by analysing the characteristics of the buggy source codes from the past and predict the present ones based on the same characteristics using statistical or machine learning models. Many studies have been conducted to predict the fault proneness of software systems. However, most of them provide either inadequate or insufficient information and thus make the fault prediction task difficult. In this paper, we present a novel set of software metrics called Error-type software metrics, which provides prediction models with information about patterns of different types of Java runtime error. Particular, in this study, the ESM values consist of information of three common Java runtime errors which are Index Out Of Bounds Exception, Null Pointer Exception, and Class Cast Exception. Also, we proposed a methodology for modelling, extracting, and evaluating error patterns from software modules using Stream X-Machine (a formal modelling method) and machine learning techniques. The experimental results showed that the proposed Error-type software metrics could significantly improve the performances of machine learning models in fault-proneness prediction.

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This paper attempts to develop an application that converts Tamil and Vietnamese speech to text, with a view to encourage usage and indirectly ensure linguistic preservation of a classical language. The application converts spoken Tamil and Vietnamese to text without auto-correction, code-mixing or code-switching. This paper proposed a complete web application, which, when perfected, could be used to act as a teaching tool to encourage correct pronunciation of syllables and words for native and non-native Tamil and Vietnamese speakers. The paper further explores similarities and differences in the two contexts.

Whenever the world is faced with a devastating outbreak of events, technology innovations have proven to be a go to solution that expedite the recovery process. We propose a mobile application rapidly developed as a contender to the TechForce19 innovation challenge, a backing of the NHSX offering £500,000 to innovators to develop digital solutions for supporting vulnerable in the community during the coronavirus outbreak. Our solution would allow people who are in selfisolation to ask for help and enable a fast, secure support by the community within 15 miles radius. The solution would allow volunteers to recognise the precise need in the community and how they can support. Within two weeks, we built a state-of-theart support application that is deployable, but with security caveats that required local authority support and resources to ensure the vulnerable are not exploited through the app.