Software Effort Prediction Using Ensemble Learning Methods

Software Cost Estimation (SCE) is an essential requirement in producing software these days. Genuine accurate estimation requires cost-and-efforts factors in delivering software by utilizing algorithmic or Ensemble Learning Methods (ELMs). Effort is estimated in terms of individual months and length. Overestimation as well as underestimation of efforts can adversely affect software development. Hence, it is the responsibility of software development managers to estimate the cost using the best possible techniques. The predominant cost for any product is the expense of figuring effort. Subsequently, effort estimation is exceptionally pivotal and there is a constant need to improve its accuracy. Fortunately, several efforts estimation models are available;however, it is difficult to determine which model is more accurate on what dataset. Hence, we use ensemble learning bagging with base learner Linear regression, SMOReg, MLP, random forest, REPTree, and M5Rule. We also implemented the feature selection algorithm to examine the effect of feature selection algorithm BestFit and Genetic Algorithm. The dataset is based on 499 projects known as China. The results show that the Mean Magnitude Relative error of Bagging M5 rule with Genetic Algorithm as Feature Selection is 10%, which makes it better than other algorithms.

Performance Analysis of the Hybrid MQTT/UMA and Restful IoT Security Model

Internet of Things (IoT) environments are being deployed all over the globe. They have the potential to form solutions to applications, from small scale applications to national and international ones. Therefore, scalability, performance, and security form a triangle of requirements that must be carefully set. Furthermore, IoT applications require higher security standards. A previously proposed IoT application framework with a security embedded structure using the integration between message queue telemetry transport (MQTT) and user-managed access (UMA) is analyzed in this work. The performance analysis of the model is presented. Comparing the model with existing models and different design structures shows that the model presented in this work is promising for a functioning IoT design model with security. The results and analysis showed that the built-in security model had performed better than models with other frameworks, especially with fog implementation.

A Hybrid IoT Security Model of MQTT and UMA

IoT applications are promising for future daily activities;therefore, the number of IoT connected devices is expected to reach billions in the coming few years. However, IoT has different application frameworks. Furthermore, IoT applications require higher security standards. In this work, an IoT application framework is presented with a security embedded structure using the integration between message queue telemetry transport (MQTT) and user-managed access (UMA). The performance analysis of the model is presented. Comparing the model with existing models and different design structures shows that the model presented in this work is promising for a functioning IoT design model with security. The security in the model is a built-in feature in its structure. The model is built on recommended frameworks;therefore, it is ready for integration with other web standards for data sharing, which will help in making IoT applications integrated from different developing parties.