Predictive Modelling of Software Behavior Using Machine Learning

Abstract

The modern software systems we use today are really
complicated which makes it tough to figure out how they will behave
when they are running. The old ways of monitoring software often
rely on looking at the code without running it or using rules to decide
what is normal. These methods are not good at finding hidden
problems or understanding how the system changes over time. To
deal with these issues this study suggests using machine learning to
predict how software will behave in environments where things are
always changing.
This approach uses machine learning to look at how the software
runs what is in the system logs and how well the system is performing
to find patterns that show abnormal behavior. It looks at things like
how it takes to execute what resources are being used what system
calls are made how much memory is being consumed and how
processes interact with each other to train models that can predict
what will happen. It uses machine learning techniques like Decision
Tree, Random Forest, Support Vector Machine and Gradient Boosting
to build models that can learn from what happened in the past.
These models are then used to predict when something might go
wrong, when the system might slow down or when the software
might behave in a way that’s not normal before it causes the system
to fail. When we tested this approach, we found that using models
together, like Random Forest and Gradient Boosting, gives us better
results and is more reliable than the old ways of doing things. This way
of predicting what software will do helps us monitor software better,
makes systems more reliable and allows us to find and fix problems
before they cause trouble.
Overall, this research shows that using machine learning to
understand software behaviour is a way to predict what software will
do, find problems and manage systems intelligently, which makes
modern software systems more stable and perform better. Machine
learning and software behaviour analysis work well together to make
software monitoring and anomaly detection more effective. This
means we can have software systems that work well and do not fail
often.
computing and things like microservices architecture.
These complicated systems produce a lot of data, like
logs and metrics, that show how the software works
under certain conditions. So it is very important to
understand and predict how software behaves. This helps
make software more reliable and work better. It also
helps prevent problems.
Traditionally, people use analysis and static testing to
monitor software. These methods often struggle to find
hidden patterns or predict what the system will do in the
future.
Predictive modelling using Machine Learning is a way to
analyze software behaviour. Machine Learning can learn
patterns from data and find relationships between things like
CPU usage and memory consumption. By looking at these
patterns, predictive models can estimate how a software
system will behave under workloads. They can also detect
when something is not normal.
Software systems often have problems like performance
degradation and unexpected crashes. If we can predict these
problems, we can reduce downtime. Save money. Machine
learning techniques can analyse large volumes of software
data. Find potential risks.
In the past few years, people have used many machine
learning algorithms like Decision Trees and Neural Networks to
analyse software behaviour. These models can find
relationships between system metrics and detect abnormal
patterns. For example, they can forecast performance
anomalies. Detect software bugs.