Software Bug Fixing Time Estimation for Software Testing Using Machine Learning Algorithm.

Estimating bug fixing time at earlier stage allows us to handle bug efficiently and save a lot of time. The issue with current manual process is that without knowing the complexity of bug, manager has to assign bug to developer based on it experience and past data which increases risk of wasting tim

Project Title

Software Bug Fixing Time Estimation for Software Testing Using Machine Learning Algorithm.

Project Area of Specialization

Artificial Intelligence

Project Summary

Estimating bug fixing time at earlier stage allows us to handle bug efficiently and save a lot of time. The issue with current manual process is that without knowing the complexity of bug, manager has to assign bug to developer based on it experience and past data which increases risk of wasting time, resources and most important delay in software delivery to client. We propose the machine learning based which will estimate the bug fixing time and helps manager to take better decisions. More precisely the model takes the data as input from a Bug Tracking System software which used for reporting, management of bugs and keep track of it. Then relevant exploratory data analysis (EDA) techniques will be applied to obtain the main characteristics of data with visual method. After that preprocessing will apply on data in which missing values will be handled and non-numeric data will be encoded to numeric so that machine learning algorithm can extract information out of it. The target will be bug fixing time and severity code will be most important attribute to estimate it. After getting trained the model will get the data about bug and estimate time to fix it.

Project Objectives

We aim to make a machine learning based model that lets the managers in software houses increasing the immersive experience and helping to take a decision which saves the time, effort, resources and most important the delivering the project on time with user satisfaction.

The key project objectives are stated here:

• Obtain the dataset from Bug tracking systems.
• Apply relevant Exploratory Data Analysis techniques.
• Apply various preprocessing techniques to handle missing values and various encoding techniques to encode non-numeric data into numeric data.
• Implement a machine learning algorithm by applying training on processed data and testing to validate it.

Project Implementation Method

Our project is based on machine learning, for that, we created a data set from bug tracking system. After creating a dataset, exploratory data analysis has to be done on data set in order to obtain its characteristics. Even after this, the data set seems not to be really true so that we apply various preprocessing techniques to handle missing values and to encode non numerical values to numerical values. algorithm by applying training on processed data and testing to validate it.

A flow chart is shown in below figure 1:

Benefits of the Project

The more we learn about machine learning, the more we appreciate the benefits of it. If we talk about applications of machine learning, there are a bundle of applications like; Machine Learning used in healthcare, military and in all advanced companies and industries. Following are benefits of our machine learning based model;

It will reduce the chances of taking wrong decisions.

It will save extra efforts.

It will save the time.

It will help to deliver project on delivery time with high quality.

Technical Details of Final Deliverable

The final product is a machine learning based model that involves the estimation of bug fixing time with more accuracy.

Final Deliverable of the Project

HW/SW integrated system

Core Industry

IT

Other Industries

Core Technology

Artificial Intelligence(AI)

Other Technologies

Sustainable Development Goals

Industry, Innovation and Infrastructure

Required Resources

If you need this project, please contact me on contact@adikhanofficial.com