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1. Base paper Title: Effective Software Effort Estimation Leveraging Machine Learning for
Digital Transformation
Modified Title: Estimating Software Effort Effectively Leveraging Digital Transformation
with Machine Learning
Abstract
Software effort estimation is a necessary component of software development projects
that belong to industrial software systems and digital transformation initiatives. Digital
transformation refers to the process of integrating digital technology into various components
of a company or organization in order to improve operations, procedures, customer
experiences, and overall performance. Industrial software systems are trained software
packages designed for use in industrial and manufacturing processes. The paper deals with the
machine learning based effort estimation in order to create an effective and robust model for
predicting effort. The paper proposes an Omni-Ensemble Learning (OEL) approach, which is
a combination of static ensemble selection along with genetic algorithm and dynamic ensemble
selection. The paper identifies the impact of software effort estimation in industrial software
system, and works on the these attributes to implement a robust ensemble model. The proposed
Omni-Ensemble Selection (OES) provides better overall performance (in terms of evaluation
metrics) and on comparing with multiple machine learning models over Finnish and Maxwell
datasets.
Existing System
The implementation of digital transformation [1] in industries is made possible, in large
part, by the use of industrial software systems. The term ‘‘digital transformation’’ refers to the
practice of adopting and integrating digital technology [2] into many elements of corporate
operations, processes, and models in order to promote innovation, enhance efficiency, improve
business performance and development and obtain a competitive edge. Industrial software
systems are specialised software programmes that have been built for industrial and
manufacturing environments. These applications offer the foundation for digitising and
automating essential activities in a variety of industries [3], including but not limited to
corporate [4], manufacturing, logistics, energy, and transportation. The following are some of
the ways that industrial software systems make digital transformation possible [5]: Automation

2. of processes; Industrial software systems make it possible to automate a wide variety of
business processes, including production planning and scheduling, inventory management,
quality control, and supply chain management. Data Management and Analytics; it makes it
easier to integrate and connect a variety of different devices, systems, and procedures inside an
industrial setting. They make it possible for the many components of an industrial ecosystem,
such as sensors, machines, control systems, and enterprise resource planning (ERP) systems,
to communicate with one another and share data and information with one another [6]. Remote
Monitoring and Control, as well as Predictive Maintenance, are a couple of the ways that
equipment failures and downtime can be anticipated and avoided. These systems are able to
spot trends and abnormalities that suggest probable failures by analysing historical data and
monitoring real-time data from industrial assets. This enables proactive maintenance and
minimises unplanned downtime. The production of digital twins, which are digital replicas of
physical assets, processes, or systems, is made possible by industrial software systems. The
ability to simulate, model, and conduct analysis on real-world scenarios is made possible by
digital twins. This assists in the optimisation of design, as well as predictive maintenance and
performance optimisation. Software Effort Estimation is an essential component of software
development projects that needs to be done, and is connected to industrial software systems
and with digital transformation initiatives as shown in Venn diagram.
Drawback in Existing System
 Data Quality and Quantity:
Insufficient Data: Machine learning models require large and diverse datasets for
training. Insufficient or poor-quality historical data can lead to inaccurate models.
Data Bias: If historical data contains biases, the machine learning model may
perpetuate and amplify those biases, leading to unfair predictions.
 Model Complexity and Interpretability:
Black Box Nature: Many machine learning models, especially complex ones like deep
neural networks, are often considered "black boxes" because their decision-making
processes are not easily interpretable. This lack of transparency can be a barrier to
understanding and trust.

3.  Resource Intensiveness:
Computational Resources: Training sophisticated machine learning models can be
resource-intensive, requiring powerful hardware and computational resources. This can
pose challenges for organizations with limited resources.
 Integration Challenges:
Integration with Development Processes: Integrating machine learning-based
estimation into existing development processes may require changes and adaptations
that can be challenging.
Proposed System
 Data Collection and Integration:
Collect historical project data, including project size, requirements, team
composition, development tools, and other relevant features.
Integrate data from various sources, ensuring data quality and consistency.
 Algorithm Selection:
Choose regression algorithms suitable for software effort estimation, considering the
specific requirements of digital transformation projects.
Evaluate algorithms such as linear regression, decision trees, random forests, support
vector machines, and neural networks.
 Integration with Project Management Tools:
Integrate the effort estimation model with project management tools, making it
seamless for project managers to access and utilize the estimates during project
planning.
 Monitoring and Maintenance:
Implement monitoring mechanisms to track the model's performance over time.
Regularly update the model to account for changes in the software development
environment and emerging technologies.

4. Algorithm
 Data Preprocessing:
Clean and preprocess the data to handle missing values, outliers, and inconsistencies.
Standardize or normalize numerical features to bring them to a common scale, as this
can improve the performance of certain algorithms.
 Hyperparameter Tuning:
Perform hyperparameter tuning to optimize the performance of the chosen algorithm.
This involves experimenting with different parameter settings to find the combination
that produces the best results. Grid search or random search techniques can be
employed for hyperparameter tuning.
 Adaptability and Continuous Learning:
Design the ML system to be adaptable and capable of continuous learning. This
involves updating the model as new data becomes available, allowing it to adapt to
changes in project dynamics and technology trends associated with digital
transformation.
Advantages
 Improved Accuracy:
Machine learning models can analyze and learn from historical data, project
specifications, and various influencing factors to provide more accurate estimates
compared to traditional estimation methods. This can lead to better planning and
resource allocation.
 Automation and Efficiency:
Machine learning enables the automation of the effort estimation process. This
reduces the manual effort required for estimation, allowing teams to focus on more
strategic and value-added activities.

5.  Risk Management:
ML models can help identify and quantify potential risks that may impact software
development efforts. By considering various risk factors, such as changing
requirements or external dependencies, the model can provide a more nuanced estimate
that accounts for uncertainties in the project.
 Transparency and Explainability:
Modern ML models can be designed to provide transparency and explainability in
their predictions. This is crucial for gaining trust from project stakeholders, as it allows
them to understand how the model arrives at its estimates and what factors contribute
to the predictions.
Software Specification
 Processor : I3 core processor
 Ram : 4 GB
 Hard disk : 500 GB
Software Specification
 Operating System : Windows 10 /11
 Frond End : Python
 Back End : Mysql Server
 IDE Tools : Pycharm