Which software estimation models moslty used in IoT applications?

In the realm of Internet of Things (IoT) applications, software estimation models are crucial for project planning, resource allocation, and timeline management. Here are some of the most commonly used software estimation models for IoT applications:

COCOMO (Constructive Cost Model):COCOMO II: This model is widely used due to its ability to handle various types of software projects, including those with embedded systems, which are common in IoT. COCOMO II considers factors like software reuse, maintenance, and the use of modern development practices.

Function Point Analysis (FPA):This method measures the functionality provided to the user based on the user’s external view of the system. It's useful in IoT for estimating the size and complexity of software by quantifying the functionalities the software provides.

Use Case Points (UCP):UCP is based on the use cases defined during the requirements gathering phase. It’s suitable for IoT applications as it helps in estimating the effort based on user interactions, which are critical in IoT systems.

Expert Judgment:Given the specialized nature of IoT applications, expert judgment is often employed. This involves consulting with experienced professionals who can provide estimates based on similar past projects and their expertise.

Delphi Method:This is a structured communication technique where a panel of experts provides estimates. It’s iterative and involves multiple rounds to reach a consensus, making it useful for complex IoT projects.

Machine Learning-based Estimation:Modern approaches include using machine learning models trained on historical project data to predict effort and cost. These models can adapt to the specific characteristics of IoT projects.

Story Points and Agile Estimation:Agile methodologies, often used in IoT development, employ story points for estimation. This involves breaking down the project into smaller tasks and estimating the effort required for each, facilitating flexibility and iterative development.

Parametric Models:These models use statistical relationships between historical data and other variables to predict outcomes. They can be tailored to the unique requirements and constraints of IoT projects.

These estimation models can be used independently or in combination to improve accuracy and reliability. The choice of model often depends on the specific characteristics of the IoT project, such as its complexity, scale, and the development methodology being employed.

Hussain Nizam

Abdulbasid Banga

Several models and techniques can be used when estimating software for Internet of Things (IoT) applications. The estimation model choice depends on your IoT project's specific context and requirements.

One of the models is RETIoT (Requirements Engineering Technology for IoT). This model provides methodological, technical, and tooling support to produce IoT software system requirements documents. Another model is the Power Estimation Model for Edge Computing Devices. While not exclusively for IoT, this methodology focuses on creating power models for edge devices and their embedded components.

The next model is the Requirements Risk Assessment Model for IoT Systems. This model considers multiple criteria, including requirements deficiencies, interrelationships, complexity, and customer priority. It aims to offer robust solutions with reduced overhead in software maintenance for IoT systems.

In addition, statistical models and machine-learning approaches can be applied in the early stages of software design. They estimate effort based on metric values from various projects. While not exclusive to IoT, these models offer valuable insights for estimation. Fine-grained energy characterization helps identify energy hotspots in software, attributing energy consumption estimates to basic components.

Raja Velusamy

In IoT applications, frequently employed software estimation models encompass COCOMO (Constructive Cost Model), Function Point Analysis (FPA), Use Case Points (UCP), Wideband Delphi, and Expert Judgment. These methodologies aid in gauging the financial, labor, and timeline aspects of software development endeavors, all customized to suit the particular needs of IoT applications.

Sandeep Kumar Bothra

Machine Learning-Based Estimation: Predicts effort and cost by analyzing historical project data, catering to the variability and complexity of IoT projects.

Expert Judgment: Relies on the experience of professionals to estimate effort and costs, especially useful when historical data is limited in the evolving IoT field.

Wideband Delphi: Uses a consensus-based approach where expert estimates are iteratively discussed and refined, leveraging collective expertise to handle the diverse nature of IoT projects.

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