Books
Permanent URI for this communityhttp://197.211.34.35:4000/handle/123456789/3
Books
Browse
2 results
Search Results
Item 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.ASoftware 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.Item Pair Programming – Cubic Prediction Model Results for Random Pairs and Individual Junior Programmers(2023-11-06) Mary Adebola Ajiboye; Matthew Sunday Abolarin; Ajiboye, Johnson Adegbenga; Abraham Usman Usman; Sanjay MisraDue to the rapidly evolving technology in the dynamic world, there is a growing desire among software clients for swift delivery of high quality software. Agile software development satisfies this need and has been widely and appropriately accepted by software professionals. The maintainability of such software, however, has a significant impact on its quality. Unfortunately, existing works neglected to consider timely delivery and instead concentrated primarily on the flexibility component of maintainability. This research looked at maintainability as a function of time to rectify codes among Individual Junior and Random pair soft ware developers. Data was acquired from an experiment performed on software developers in the agile environment and analyzed to develop the quality model metrics for maintainability which was used for prediction. One hundred programmers each received a set of agile codes created in the Python programming language, with deliberate bugs ranging from one to ten. The cubic regression model was used for predicting time spent on debugging errors above ten bugs. Results show that the random pair programmers spent an average time of 21.88 min/error while the individual programmers spent a lesser time of 16.57min/error.