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Enhancing software fault prediction with deep neural networks: An empirical analysis of error-type metrics (2023)
Conference Proceeding
Phung, K., Ogunshile, E., & Aydin, M. E. (in press). Enhancing software fault prediction with deep neural networks: An empirical analysis of error-type metrics.

In the context of software quality assurance, Software Fault Prediction (SFP) serves as a critical technique to optimise costs and efforts by classifying software modules as faulty or not, using pertinent project characteristics. Despite considerable... Read More about Enhancing software fault prediction with deep neural networks: An empirical analysis of error-type metrics.

Error-type -A novel set of software metrics for software fault prediction (2023)
Journal Article
Phung, K., Ogunshile, E., & Aydin, M. (2023). Error-type -A novel set of software metrics for software fault prediction. IEEE Access, 11, 30562-30574. https://doi.org/10.1109/ACCESS.2023.3262411

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... Read More about Error-type -A novel set of software metrics for software fault prediction.

Hive (2021)
Exhibition / Performance
Farzadnia, F. Hive. [Modular kinetic architecture]. Exhibited at Online at the Annual Fifteen show. 19 February 2021 - 5 March 2021. (Unpublished)

Hive is a modular kinetic architecture inspired by bee colony behaviour. Certain types of Mexican honeybees produce a wavelike cascade by ‘shimmering’ when they feel threatened. When a wasp or another big insect come too close to the hive, the honeyb... Read More about Hive.