Browsing by Author "Ajiboye M.A"

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DEVELOPMENT OF AGILE PRODUCTIVITY METRICS OF INDIVIDUAL EXPERT JAVASCRIPT DEVELOPERS FOR SOFTWARE PROJECT MANAGERS
(Humminbird Publications and Research International, 2024-01-29) Abdulgafar A.; Makinde J.K; Ajiboye, Johnson Adegbenga; Ajiboye M.A
Software Project Managers require metrics to measure productivity in team work. Since agile software development require continuous improvement, metrics helps in identifying bottlenecks and inefficiencies thereby enabling teams to refine their processes iteratively. Productivity metrics also helps in effective resource allocation and optimization to ensure timely delivery of software products by Software Project Managers. Although metrics have been developed for traditional software programmers little work has been done in developing metrics for Agile Software Project Managers specifically for JavaScript Program. In this work, metrics for Individual Expert agile software programmers and specifically for JavaScript was developed. Programs in JavaScript was designed and developed to record the time spent in correcting deliberate errors introduced. Experiment was conducted among one hundred programmers' group of Individual Expert pairs with the aim of recording time spent in debugging the codes, The curve fit regression models of time spent in debugging a number of bugs in agile software written in JavaScript programming language for project managers revealed that Cubic model had the highest R squared value of 0.996 which is closely followed by the quadratic model with a value of 0.980 while the compound, growth and exponential models have the least value of 0.868.
DEVELOPMENT OF MODEL METRICS FOR INDIVIDUALS AND PAIR PROGRAMMERS AMONG SOFTWARE DEVELOPERS IN AN AGILE ENVIRONMENT
(2023) Ajiboye M.A; Ajiboye, Johnson Adegbenga; Audu W.M; Ajiboye D.J; Ohize H.O; Majin R.N; Abolarin M.S
In this work, maintainability as a function of time to correct codes was examined among various categories of software developers. Deliberate errors, ranging from two to ten, were introduced into sets of agile codes written in python programming language and given to 100 programmers each in the groups of Individual Junior, Individual Expert, Random, Expert pairs, junior pairs and Junior Expert pairs. The time spent to correct the errors was analysed using regression model for prediction. Bivariate correlation was used to check the relationships between the number of bugs in projects and the time spent to correct the errors. The correlation between the number of bugs and time of debugging was highly significant, strong and positive. This revealed that the time spent in correcting system software errors increased significantly as the number of bugs increased. Linear, logarithmic, inverse, quadratic, cubic and exponential regression models were used to generate metrics with time spent on error as dependent variable and number of bugs as independent variable for each of the pair and individual programmers. On the average, cubic model gave the highest R2 value of 0.639 in comparison to other models. Therefore, Cubic model gave the best fit as it explains the patterns of the relationship between the dependent and independent variable most appropriately.
DSP in Communication Engineering - A Review
(I3C 2024, 2024-04-22) Ajiboye, Johnson Adegbenga; Jiya Z.J; Paul M.; Ajiboye M.A; Ajiboye D.J; Majin R.N
This paper provides a comprehensive review of Digital Signal Processing (DSP) in communication engineering, elucidating its fundamental principles, practical applications, and recent advancements. Beginning with an overview of DSP's distinguishing features and historical evolution, the paper delineates its pivotal role in processing real-world signals, including speech, image, and seismic data. Furthermore, the introduction of Software Defined Radio (SDR) is examined, underscoring its transformative impact on communication systems by enabling dynamic spectrum access and multi-standard operation through DSP algorithms. Additionally, the emergence of Quantum Signal Processing is explored, highlighting its significance in secure communication through Quantum Key Distribution (QKD) and Quantum Error Correction. Despite the benefits offered by DSP, challenges such as computational complexity and signal distortions are addressed, emphasizing the need for advanced techniques and algorithms to mitigate these issues. Ultimately, this paper elucidates DSP's enduring relevance and innovation in shaping the future of communication engineering.
HYBRID AUTOREGRESSIVE NEURAL NETWORK (ARNN) MODEL FOR SPECTRUM OCCUPANCY PREDICTION
(NJEAS, 2022) Ajiboye, Johnson Adegbenga; Adegboye B.A; Aibinu A.M; Kolo J.G; Ajiboye M.A; Usman A.U
A secondary spectrum user cannot transmit in a channel before sensing and knowing the spectrum occupancy state as this may cause interference. This poses a major challenge because these operations ought to be performed in each time slot and thereby causing a substantial delay before the user gains access to the spectrum, leading to inefficient utilization. Therefore, a channel predictive system will mitigate this problem. In this work, an ensemble machine learning model for spectrum occupancy prediction was developed. The developed model was trained using a sample of Power Spectrum Density (PSD) data collected from the field for a period of twenty four hours within a frequency range of 30-300 MHz. The frequency range was grouped into sub bands. Based on the training data and the corresponding output data, the neural network model trains itself to come up with the best weights which can generally be used by the AR model for unseen data. After computing the weights, the performance was first tested on the entire training data, on the validation dataset and on the test dataset. Prediction results revealed an overall accuracy of 98.32% with band 4 (74.85-87.45 MHz) having the highest accuracy of 99.01% and the lowest accuracy of 89.39% in band 2 (47.05-68 MHz).