All Outputs (5)

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.

Modeling diseases with Stream X Machine (2021)
Conference Proceeding
Jayatilake, S., Ogunshile, E., Aydin, M., & Phung, K. (2021). Modeling diseases with Stream X Machine. In 2021 9th International Conference in Software Engineering Research and Innovation (CONISOFT) (61-68). https://doi.org/10.1109/CONISOFT52520.2021.00020

At present the world is moving towards alternative medicine and behavioural alteration for treating, managing, and preventing chronical diseases. With the individuality of the human beings has added more complexity in a domain where very high accurac... Read More about Modeling diseases with Stream X Machine.

A novel software fault prediction approach to predict error-type proneness in the Java programs using Stream X-Machine and machine learning (2021)
Conference Proceeding
Phung, K., Ogunshile, E., & Aydin, M. (2021). A novel software fault prediction approach to predict error-type proneness in the Java programs using Stream X-Machine and machine learning. In 2021 9th International Conference in Software Engineering Research and Innovation (CONISOFT) (168-179). https://doi.org/10.1109/CONISOFT52520.2021.00032

Software fault prediction makes software quality assurance process more efficient and economic. Most of the works related to software fault prediction have mainly focused on classifying software modules as faulty or not, which does not produce suffic... Read More about A novel software fault prediction approach to predict error-type proneness in the Java programs using Stream X-Machine and machine learning.

An algorithm for implementing a minimal stream X-Machine model to test the correctness of a system (2020)
Conference Proceeding
Ogunshile, E., & Phung, K. (in press). An algorithm for implementing a minimal stream X-Machine model to test the correctness of a system. In 2020 8th International Conference in Software Engineering Research and Innovation (CONISOFT). https://doi.org/10.1109/CONISOFT50191.2020.00023

The rapid change of requirements has made software more complex and harder to maintain. Software testing tools play an important role in the Software Development Life Cycle. However, many technology companies have employed fast paced development of s... Read More about An algorithm for implementing a minimal stream X-Machine model to test the correctness of a system.

Covid-19 Care – A mobile application to help connect volunteers and vulnerable people in the community during the Covid-19 lockdown (2020)
Conference Proceeding
Ogunshile, E., Phung, K., & Odongo, S. (in press). Covid-19 Care – A mobile application to help connect volunteers and vulnerable people in the community during the Covid-19 lockdown. In 2020 8th International Conference in Software Engineering Research and Innovation (CONISOFT) (124-133). https://doi.org/10.1109/CONISOFT50191.2020.00027

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 innov... Read More about Covid-19 Care – A mobile application to help connect volunteers and vulnerable people in the community during the Covid-19 lockdown.