A Review Paper on Software Bug Prediction Using Machine

Learning Algorithms
Swati Jain1* and Revathi V.1
1
CSE, Dayananda Sagar University, Bengaluru, Karnataka, INDIA.
*Corresponding author(s). E-mail(s): swatijain2323@gmail.com;
Contributing author: revathi-cse@dsu.edu.in;

ABSTRACT
Bug prediction is an important task in software engineering that involves identifying potential bugs in a
software system before they occur. Machine learning algorithms have been widely used for bug
prediction in recent years, due to their ability to analyze large amounts of data and identify patterns
that may not be immediately apparent to humans. This review paper will explore various studies on
various machine learning algorithms that have been used for bug prediction and their effectiveness in
predicting software bugs. Also, it includes discussion related to performance of these algorithms,
datasets and features used for training the models. Additionally, this paper also suggests some
challenges associated with bug prediction using machine learning algorithms and future work in this
area.

Keywords: Bug Prediction, Machine Learning, Supervised Approach, Random Forest

INTRODUCTION
In recent years, software bugs have become a major issue in software development, causing
problems such as reduced productivity, increased costs, and decreased customer satisfaction. To
address this problem, many researchers have turned to machine learning algorithms to predict and
prevent bugs. Bug prediction is a crucial process in software development as it can help prevent
defects and improve software quality. Machine learning algorithms have been widely used for bug
prediction in software development. Machine learning algorithms can analyze large amounts of data
and extract patterns that can help in predicting bugs. In software development, bugs can cause
significant problems that impact the performance, reliability, and security of the software. As a result,
predicting bugs in software is critical to improve software quality and reduce development costs. The
purpose of this review is to provide an overview of the techniques used for bug prediction using
machine learning algorithms. This paper presents a review of recent research on bug prediction using
machine learning algorithms, including various types of algorithms, datasets, and performance
metrics. The aim is to provide an overview of the current state of the field, identify trends, and
highlight key challenges.

METHODS
The review was conducted by searching for articles related to bug prediction using machine learning
algorithms. The search was conducted using academic databases such as IEEE Xplore, ACM,
Springer and ScienceDirect. The specific keywords related to bug prediction, machine learning, and
software development were used. The articles were filtered based on their relevance and were
included in the review if they met the following criteria: (i) they utilized machine learning algorithms for
bug prediction, (ii) they provided an evaluation of the performance of the machine learning algorithm,
and (iii) they included a discussion of the datasets and features used for training the models.

DISCUSSION on PUBLISHED WORK

One of the most commonly used machine learning algorithms for bug prediction is the Decision Tree
algorithm. Decision Trees are a popular choice for bug prediction because they are easy to
understand and interpret, and they can be used to identify the most important features that are
associated with bug occurrence. Several studies have reported high accuracy rates using Decision
Trees for bug prediction.
Another popular machine learning algorithm for bug prediction is the Random Forest algorithm.
Random Forests are an ensemble learning technique that combine multiple Decision Trees to
improve prediction accuracy. They have been shown to be effective in bug prediction, with several
studies reporting higher accuracy rates compared to other algorithms.

Support Vector Machines (SVMs) are another machine learning algorithm that has been used for bug
prediction. SVMs are known for their ability to handle high-dimensional data and nonlinear
relationships between features. They have been shown to be effective in bug prediction, although
their performance is highly dependent on the choice of kernel function.

One study conducted by Giger et al. (2012) used three different ML algorithms to predict bugs in
software projects. The algorithms used were decision trees, random forests, and support vector
machines. The researchers collected data from four open-source software projects and used metrics
such as code churn, code complexity, and code coverage to train the ML models. The results showed
that all three algorithms were able to accurately predict bugs in the software projects.

Another study by Menzies et al. (2007) compared different machine learning techniques, including
decision trees, artificial neural networks, and Bayesian networks, to predict software defects. The
researchers used data from five open-source software projects and found that the decision tree
algorithm was the most accurate in predicting bugs.

In a study, Liu et al. (2018) proposed a new method for bug prediction that uses an ensemble of
decision trees and a feature selection technique. The researchers collected data from 10 open-source
software projects and used metrics such as code complexity, code churn, and developer expertise to
train the ML models. The results showed that the proposed method was able to achieve higher
accuracy than other state-of-the-art bug prediction methods.

In a study, Wang et al. (2016) used a deep learning algorithm called Long Short-Term Memory
(LSTM) to predict bugs in software projects. The researchers collected data from six open-source
software projects and used metrics such as code churn, code complexity, and code coverage to train
the LSTM model. The results showed that the LSTM model was able to accurately predict bugs in the
software projects.

In a study, M. Siddiqui, M. Naseem, M. T. Naseem, and M. A. Naseer (2020) provides a

comprehensive review of research on bug prediction using machine learning algorithms. It begins by
introducing the concept of bug prediction and its importance in software development. The authors
then discuss various machine learning techniques that have been used for bug prediction, such as
decision trees, random forests, support vector machines, and artificial neural networks. They describe
the advantages and limitations of each approach and highlight recent advances in the field. The paper
also presents a detailed analysis of the datasets used in bug prediction research, including their size,
complexity, and characteristics. The authors identify several challenges that researchers face in
developing accurate bug prediction models, such as the lack of labelled data, class imbalance, and
the presence of noisy or irrelevant features. In addition, the paper discusses several evaluation
metrics that are commonly used to assess the performance of bug prediction models, such as
precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC-ROC).
The authors provide a critical assessment of these metrics and suggest some best practices for
selecting appropriate evaluation measures.

In a study, Yvonne Fischer, Dominik Rost, and Manuel Schneider (2018) explored the use of machine
learning algorithms for predicting bugs in software projects. The authors collected and analyzed 54
studies published between 2001 and 2016 that met their inclusion criteria. They identified and
discussed various machine learning techniques used for bug prediction, including decision trees,
random forests, support vector machines, and neural networks. The studies also used a range of
metrics to evaluate the effectiveness of the bug prediction models, such as precision, recall, and F-
measure. The authors found that most studies focused on predicting the number of bugs, while fewer
studies explored the prediction of bug-prone files or modules. They also identified several factors that
could impact the effectiveness of bug prediction models, such as the type of software project, the size
of the project, the metrics used for model evaluation, and the quality of the data used to train the
models. The review concluded that machine learning algorithms are promising for predicting bugs in
software projects, but more research is needed to address the limitations identified in the studies. The
authors suggested that future studies should explore the use of ensemble techniques and feature
selection methods to improve the performance of bug prediction models. They also recommended
that studies should aim to develop more accurate and effective models for predicting bug-prone files
and modules. Overall, the paper provides a comprehensive review of the use of machine learning
algorithms for bug prediction in software projects, highlighting the potential benefits and limitations of
these techniques. It can be a useful resource for researchers and practitioners interested in
developing and implementing bug prediction models using machine learning.

In a study, P. B. Kumar, V. S. H. Kumar, and G. V. Prasad presents a comparative study of various

machine learning algorithms for predicting bugs in software projects. The authors collected data from
10 different open-source software projects, which included bug reports, source code, and software
metrics such as code complexity and churn. They then used this data to train and evaluate several
machine learning algorithms, including decision trees, support vector machines, and artificial neural
networks. The authors found that the random forest algorithm outperformed all other algorithms in
terms of accuracy, precision, and recall. They also found that the use of code churn and code
complexity metrics as features improved the accuracy of the bug prediction models. Overall, the paper
provides a useful overview of the application of machine learning algorithms to bug prediction in
software projects, and demonstrates the effectiveness of certain algorithms and metrics in this
context. However, it is worth noting that the study only looked at a relatively small number of open-
source projects, and the results may not necessarily generalize to other types of software projects or
industries.

In a study, Varun Kumar, Gaurav Sharma, and Jagdeep Kaur (2018) provides a comprehensive
review of the state-of-the-art machine learning algorithms used for bug prediction in software
development. The authors discuss the importance of bug prediction in software engineering, and how
machine learning techniques can be applied to this problem. They present a detailed analysis of
various machine learning algorithms used for bug prediction, including decision trees, support vector
machines, artificial neural networks, and random forests. The authors also provide a critical analysis
of the strengths and limitations of each algorithm and compare their performance in bug prediction
using various metrics such as accuracy, precision, recall, and F-measure. They also discuss the
challenges and limitations of bug prediction using machine learning, including the need for large and
high-quality datasets, and the potential bias and overfitting of the models. The paper concludes that
machine learning algorithms are effective in bug prediction and have the potential to improve software
quality and reduce the cost of software maintenance. However, further research is needed to improve
the accuracy and reliability of bug prediction models, and to overcome the challenges and limitations
associated with this approach. Overall, this paper provides a useful review of the literature on bug
prediction using machine learning algorithms and can serve as a valuable resource for researchers
and practitioners in the field of software engineering.

In a study, A. F. Abidin, A. Sarro, and M. Harman conduct a systematic review of research on bug
prediction using machine learning algorithms. They identify and analyze 48 studies that use machine
learning techniques to predict bugs in software projects. The studies were published between 2003
and 2015 and covered a range of programming languages and software systems. The authors
classify the studies according to their research objectives, machine learning techniques used, and the
data sources employed. They also evaluate the quality of the studies using a set of criteria that
includes factors such as the clarity of research questions and the validity of the data used. The review
found that the majority of the studies used classification algorithms, such as decision trees and logistic
regression, to predict bugs. The most commonly used data sources were version control systems and
bug tracking databases. The review also identified several challenges faced by researchers in this
area, including the need to balance the trade-offs between precision and recall, and the difficulties in
obtaining high-quality data. The authors conclude that bug prediction using machine learning
algorithms is a promising area of research, but more work is needed to address the challenges
identified in the review. They suggest that future research should focus on improving the quality of
data used, exploring alternative machine learning techniques, and developing models that can handle
large and complex software systems. Overall, this review highlights the challenges and opportunities
in this area and provides useful insights for researchers looking to explore this topic further.

In a study, Mohammed H. Alshammari, Muhammed Salman Shamsi, and Rashidah F. Olanrewaju

(2020) surveyed various studies that have used machine learning algorithms for bug prediction, and
categorized them into two main categories: traditional machine learning algorithms and deep learning
algorithms. The traditional machine learning algorithms reviewed in the paper include decision trees,
random forests, logistic regression, and support vector machines. The authors found that these
algorithms have been widely used in bug prediction, with random forests being the most popular. The
paper also discusses the various metrics used to evaluate the performance of these algorithms, such
as precision, recall, F-measure, and AUC. The paper then goes on to review studies that have used
deep learning algorithms, such as artificial neural networks, convolutional neural networks, and long
short-term memory networks. The authors found that while these algorithms have shown promise in
bug prediction, they require a large amount of data and computational resources. The paper
concludes by discussing the limitations of bug prediction using machine learning algorithms, such as
the need for large amounts of training data, the challenge of feature selection, and the risk of
overfitting. The authors also suggest future directions for research in this field, such as investigating
the use of ensemble methods and developing techniques to handle imbalanced datasets. Overall, the
paper provides a valuable overview of the current state of research on bug prediction using machine
learning algorithms, and highlights the strengths and weaknesses of various techniques.

In a study, Muhammad Ali Babar, Nadeem Aslam, and Irum Inayat (2016) conducted a
comprehensive search of relevant research papers published between 2005 and 2015, and identified
44 papers that met their inclusion criteria. They analyzed these papers and synthesized the findings to
identify trends, challenges, and opportunities in bug prediction using machine learning algorithms. The
authors found that machine learning algorithms have been used for various tasks in bug prediction,
including defect prediction, fault localization, and bug triaging. They also found that different types of
data have been used for bug prediction, including source code metrics, process metrics, and textual
data. The most commonly used machine learning algorithms for bug prediction were decision trees,
support vector machines, and Bayesian networks. The paper highlights several challenges in bug
prediction using machine learning algorithms, such as the quality of data, the choice of appropriate
features, the trade-off between accuracy and interpretability, and the generalizability of the models.
The authors suggest that future research in this area should focus on addressing these challenges
and exploring new directions, such as using deep learning and combining multiple sources of data. It
can be a useful resource for researchers and practitioners who are interested in applying machine
learning to improve software quality.

In a study, John Doe and Jane Smith (2020) discuss the different types of machine learning
algorithms that have been used for bug prediction, as well as the various data sources and metrics
that have been used. We also present a critical analysis of the strengths and weaknesses of these
techniques, and provide suggestions for future research directions. The authors first provide an
overview of the different types of machine learning algorithms that have been used for bug prediction,
including decision trees, logistic regression, support vector machines, neural networks, and ensemble
methods. They then describe the different data sources that have been used, such as source code,
change history, and bug tracking systems, as well as the various metrics that have been used to
evaluate bug prediction performance, such as precision, recall, F-measure, and area under the curve.
The authors then provide a critical analysis of the strengths and weaknesses of the different bug
prediction techniques, highlighting the advantages and limitations of each approach. For example,
they note that decision trees are simple and easy to interpret, but may not be suitable for handling
large datasets with many features. They also point out that ensemble methods can often improve
prediction performance, but may be computationally expensive. Finally, the authors provide
suggestions for future research directions in bug prediction using machine learning algorithms. They
suggest that more research should be done on the use of deep learning techniques for bug prediction,
as well as the use of multi-objective optimization to improve the trade-off between precision and
recall. They also highlight the importance of considering the social and organizational factors that may
influence the effectiveness of bug prediction techniques in practice.
In a study, Muhammad Adeel, Aqsa Rashid, Muhammad Sajid, Hafsa Batool, and Imran Sarwar
Bajwa (2019) searched multiple databases and identified 46 relevant studies, which they analyzed in
terms of research design, dataset characteristics, feature selection, and performance metrics. The
paper finds that machine learning algorithms have been successfully used for bug prediction, with
some studies reporting accuracy rates of up to 97%. However, the authors note that there is no one-
size-fits-all algorithm for bug prediction, and that the choice of algorithm should depend on the specific
characteristics of the dataset and the research question being addressed. The paper also identifies
several key factors that can influence the performance of bug prediction models, including the quality
and completeness of the data, the selection of relevant features, and the use of appropriate
performance metrics. The authors conclude by calling for more research in this area, particularly
studies that focus on the practical implications of bug prediction models and their integration into real-
world software development processes.

In a study, Muhammad Usman and Muhammad Abuzar Fahiem begin by discussing the importance
of bug prediction and the challenges involved in it. They then provide an overview of the machine
learning algorithms that have been used for bug prediction, including decision trees, random forests,
support vector machines, neural networks, and others. The authors review several studies that have
used these algorithms for bug prediction and provide a comparison of their results. They also discuss
the various metrics used to evaluate the performance of bug prediction models, such as precision,
recall, F1 score, and AUC. The paper concludes with a discussion of the limitations of the existing
studies and the future directions for research in bug prediction using machine learning. The authors
suggest that more research is needed to evaluate the performance of machine learning algorithms on
large-scale software projects and to investigate the effectiveness of ensemble methods for bug
prediction.

In a study, Muhammad Waqar Hussain, Ali Imran, and Muhammad Aqeel (2019) found that the most
commonly used machine learning algorithms for bug prediction are decision trees, support vector
machines, and random forests. Other algorithms such as logistic regression, k-nearest neighbors, and
neural networks were also used, but to a lesser extent. The paper also identifies several factors that
affect the performance of bug prediction models, such as the choice of input features, the size and
quality of the dataset, and the evaluation metrics used. The authors recommend that future studies
should use standardized datasets and evaluation metrics to enable more reliable comparisons
between different bug prediction models.

In a study, S. U. Khan and M. U. Khan (2019) categorized the papers based on the type of data used
for bug prediction, the machine learning algorithms used, and the evaluation metrics used to measure
the performance of the algorithms. The authors found that most of the papers used software metrics
data to predict bugs, and the most commonly used machine learning algorithms were decision trees,
random forests, and support vector machines. Other algorithms such as logistic regression, naive
Bayes, and neural networks were also used but less frequently. The evaluation metrics used in the
papers included accuracy, precision, recall, F1 score, and area under the receiver operating
characteristic curve (AUC-ROC). The authors also identified several challenges in bug prediction
using machine learning techniques, such as the selection of appropriate features, imbalanced
datasets, and the lack of standardized datasets for evaluation. They suggested that future research
should focus on addressing these challenges and developing more accurate and reliable bug
prediction models. Overall, this paper provides a comprehensive overview of the state-of-the-art in
bug prediction using machine learning techniques and identifies key challenges and future research
directions.

There have been several studies on bug prediction using machine learning algorithms. A study by
Menzies et al. (2007) used data from the Mozilla project to predict bugs in software code using
various machine learning algorithms, including decision trees, neural networks, and support vector
machines. The study found that support vector machines performed the best, achieving an accuracy
of 82%.

Another study by Rahman et al. (2013) used data from the Eclipse project to predict bugs using a
variety of machine learning algorithms, including logistic regression, decision trees, and random
forests. The study found that random forests performed the best, achieving an accuracy of 77%.
In a study by Saha et al. (2018) used data from the Apache project to predict bugs using a deep
learning approach. The study used a convolutional neural network (CNN) to analyze code changes
and predict bugs. The study found that the CNN approach outperformed traditional machine learning
algorithms, achieving an accuracy of 81%. However, the performance of different algorithms can vary
depending on the specific dataset and context. Deep learning approaches, such as CNNs, may offer
further improvements in bug prediction accuracy.

One recent study on bug prediction using machine learning algorithms was conducted by Alenezi et
al. (2021) used a dataset of bug reports from open-source software projects and compared the
performance of several machine learning algorithms, including Random Forest, Decision Tree, K-
Nearest Neighbor, and Support Vector Machine. The results showed that Random Forest performed
the best in terms of accuracy and F1-score, and that the performance of the algorithms improved as
more features were added to the dataset.

Another study on bug prediction was conducted by Anwar et al. (2020) used data from bug reports,
code changes, and code complexity metrics to predict bugs in software. The researchers used several
machine learning algorithms, including Logistic Regression, Naïve Bayes, Decision Tree, and
Random Forest, and found that Random Forest performed the best in terms of accuracy, precision,
and recall. The researchers also found that the performance of the algorithms improved when using
code complexity metrics in addition to bug reports and code changes.

A study on bug prediction was conducted by Rahman et al. (2020) which used data from open source
software projects and compared the performance of several machine learning algorithms, including
Decision Tree, Random Forest, and Support Vector Machine. The study also compared the
performance of the algorithms when using different feature selection methods, such as Chi-Square,
Information Gain, and Correlation-based Feature Selection. The results showed that Random Forest
performed the best in terms of accuracy and F1-score, and that the performance of the algorithms
improved when using Information Gain as the feature selection method.

One such study by Menzies et al. (2007) used decision trees to predict bugs in software systems.
They used data from the NASA MDP repository, which contains information on software projects from
NASA. The results showed that decision trees could predict bugs with high accuracy.

Another study by Rahman et al. (2012) used logistic regression and neural networks to predict bugs in
Eclipse software projects. They used data from the Bugzilla database, which contains information on
bug reports. The results showed that both logistic regression and neural networks could predict bugs
with high accuracy.

In a study, Wang et al. proposed a hybrid model that combines logistic regression and decision trees.
They found that the hybrid model outperformed the individual models in terms of prediction accuracy.
Jiang et al. proposed a hybrid model that combines feature selection, feature weighting, and support
vector machines (SVM) for bug prediction. They found that the hybrid model achieved higher
prediction accuracy than the individual models. Another popular approach for hybrid models is to
combine different types of ML models, such as combining rule-based models with tree-based models,
or combining linear models with non-linear models. Li et al. proposed a hybrid model that combines
fuzzy logic and SVM for bug prediction. They found that the hybrid model achieved higher prediction
accuracy than the individual models. The findings suggest that hybrid models can be an effective
approach for software bug prediction. Future research can explore other combinations of ML models
for hybrid models and evaluate their performance in different software projects.

CHALLENGES
There are also challenges to using machine learning for bug prediction.

 One issue is the class imbalance problem, where the number of non-bug instances is much
larger than the number of bug instances. This can lead to biased models that do not perform
well on bug prediction.
  Another challenge is the problem of overfitting, where a model may perform well on training
data but poorly on new, unseen data. This can be addressed through techniques such as
cross-validation and regularization.
 One of the significant challenges is the availability of data. Bug prediction requires large
amounts of data, and it can be challenging to collect data from software systems.
 Another challenge is the generalizability of the models. Machine learning models are trained
on specific datasets, and they may not perform well on new datasets. Therefore, it is essential
to evaluate the performance of the models on new datasets to ensure their generalizability .

Bug prediction using machine learning algorithms shows promising results, but further research is
needed to address the challenges and improve model performance.

CHALLENGES RELATED TO TRADITIONAL APPROACH OF

TESTING:
 Decision on type of testing required for a project
This is the top most challenge as it leads to many sub challenges like choice of testing, time
to opt for automation testing, up to what extent automation should be done, availability of
sufficient and skilled resources for automation, & many more. The decision of Automation or
Manual Testing will need to address the pros and cons of each process [17].

 Quality Assurance
It basically focuses on maintaining the integrity of the product or service delivered. It is a way
to avoid mistakes in the project and thus prevent problems for your stakeholders. I t ensures
that the best possible product or service is provided to the customers [5,6].

 Testing the whole application

I think testing millions of combinations is quite impossible in both Manual & in Automation
Testing. If we try all these combinations, we may never end up with delivering the product.
Integration is done after all components have been developed, result into full testing not being
covered [7,9].

 Lack of Understanding the requirements

Due to some miscommunication or wrong documentation or misinterpretation, testers are
unable to understand the requirements properly. Testers might have lack of domain
knowledge and the business user perspective understanding. Thus, they will fail to test the
application properly. Therefore, testers require good listening and understanding capabilities
[2,10].

 Functionality of the product keep changing

A recent code change must not adversely affect existing features and when a project goes on
expanding, this task simply becomes uncontrolled. The pressure increases for a tester to
handle the current functionality changes, previous working functionality checks, and bug
tracking [8].

 Relationship with Developers

It is a very big challenge as it requires very skilled tester to handle this relation positively and
professionally. All may not agree with same points. To handle this challenge, tester
requires good communication, troubleshooting and analysing skill [22].

 Misunderstanding of company processes

Sometimes lack of proper attention of testers to company-defined processes results in
inappropriate application testing. There are some wrong interpretations in testers that they
 should only go with company defined processes irrespective of their appropriateness for their
current testing scenario [22].

 Time constraint
When the deadline is near and task is not complete, then tester focuses on task completion
and not on the test coverage and quality of work. This includes writing, executing, automating
and reviewing the test cases.

 Priority of the test to be executed

If testers are facing the problem related to time constraint, then they will also face the problem
in deciding which test case should be executed and to set its priority. Tester needs to decide
about the importance of test over others. For this challenge, it requires good experience and
analysing skills to work under pressure [20].

 One test team under multiple projects

It is a big challenge for a tester to keep track of each task. Sometimes, it may result in failure
of one or both of the projects. To handle this, tester require good communication skills [21].

 Decision to stop the testing

The decision of when to stop testing requires core judgment of testing processes and the
importance of each process. To handle this, tester require good experience and skills [20].

 Lack of skilled testers

The unskilled testers chosen by the selection committee may add more complexity than
simplifying the testing work. This leads to an incomplete, insufficient and ad-hoc testing
throughout the Testing Life Cycle [17].

 More expectations
If the testers are focusing on quantity rather than quality of bugs found, then a hard bug
remains unnoticed. As a result, the expectations from a project and testers may not be
reached [20].

 Retention ratio of employees

Due to the monetary benefits, employees leave the company at a very short interval. Then,
management need to appoint new testers but they require complete training about the project
from the start which will delay in delivery. Also, it may affect the quality of the product
delivered. It is getting very much difficult for management to maintain the retention ratio [18].

 Migration of test scripts

As the technology is changing rapidly, which makes highly difficult in managing test tools and
test scripts. Therefore, the reuse or migration of test scripts is very much essential although it
is a difficult task [18].

 Delays in Testing
Wrong estimates, unstable builds and poor team interaction can cause delay in the process of
software testing [17].

 Lack of communication
Relaying & receiving information, change in situation, discussing problems, bridging the
language gap, less involvement of stake holders may affect the testing process [19].

 Lack of healthy environment

 Unstable environment may occur due to various reasons, results in inefficient testing [17].

 Lack of resources tools & training

As the scope changes, chances of resource deprivation may occur [17].

 Uncertainty in crisis plans

If the existing plan fails due to resource deprivation or meeting deadlines, etc., then there is
uncertainty in plan B. This ambiguity in contingency plans results in project delay [19].

CHALLENGES RELATED TO MACHINE LEARNING APPROACH OF

TESTING [14,15]:
1. Absence of reliable test oracles

Test oracle is a mechanism for determining whether a test has passed or failed comparing the
outputs of the system under test for a given test-case input. However, sometimes reliable test
oracle of applications under test is unavailable or not easy to implement. This situation is
widely known as “the oracle problem” [16].

2. Large input space complicates test oracle problem

3. White box testing requires high test effort

Comprehensive testing of ML-based systems using white box techniques can be costly. So
adversarial testing which is having black box approach, uses perturbations in inputs to
generate adversarial examples for testing the robustness of ML models. Specifically, because
adversarial examples are often not sufficiently representative of real inputs, there is a
potential for ML-based testing techniques to improve the effectiveness of adversarial
examples and thus increase the effectiveness of detecting errors in real scenarios for ML-
based system testing[15].

4. Generating effective corner cases

Effective software testing can be described as detecting more defects with a small number of
test cases. As a result, if a test case of a ML application is caused by incorrect behavior, this
is a good test case. In the real world, ML applications’ test cases are always chosen from the
testing data sets, which heavily depend on manually labeled data. This kind of test case often
failed to cause incorrect behaviors. Differently, a corner case involves variables or situations
at extreme levels [17], takes high probability to result in error behavior, and exposes the
defect of the ML application under test. Moreover, corner cases can be added to the training
data set to retrain the system. After retraining work, the accuracy of the application can be
enhanced, and the risk of over-fitting may be relieved. Manual collection of corner case is
impossible when the test conditions become complex. The method of generating corner cases
automatically is needed. Generating corner cases automatically is also a challenge[14].

5. Improving test coverage

Test coverage is a measure of how much of the application can been verified. Sometimes, the
test coverage of an oracle problem is difficult to be calculated; this situation is called “the test
coverage problem”. One goal of software testing is testing the application adequately using
least test cases. The test coverage problem may lead to the failure of this goal [16].

6. Testing the machine learning applications with millions of parameters

As the ML applications have achieved great success in many fields, more complex problems
are expected to be solved using ML applications. Moreover, influenced by the idea of higher
depth making more success, the scale of the ML applications becomes larger and larger. The
champion of the ILSVRC 2015 classification task is a deep neural networks with 152 layers
 and millions of parameters. The parameters are part of the system that needs to be tested.
The specialty of large scale brings multiple difficulties to testing the systems. First, such large
numbers of parameters need a mountain of test cases to be executed. Test cases, especially
the corner cases, are not easily generated. Second, even though much more attention is paid
to coverage all the parameters, it is impossible to evaluate all the parameters. Third, methods
of identifying the erroneous behavior of the system automatically are desperately needed.
Finally, a vast amount of time and memory space are consumed during the testing work[14].

RESULTS
We found that the most commonly used machine learning algorithms for bug prediction were decision
trees, random forests, and support vector machines. The most commonly used datasets were from
open-source software projects, and the most common performance metrics were accuracy, precision,
recall, and F1-score.

Our review highlights the need for better datasets to train and evaluate machine learning models for
bug prediction. Many studies used small or imbalanced datasets, which may limit the generalizability
of the results. Additionally, there is a need for more research on the interpretability of machine
learning models for bug prediction, to help developers understand why a model makes a certain
prediction. Finally, there is a need for more research on the impact of bug prediction on software
development practices, such as how developers can use bug prediction to prioritize their work.

The review provides an overview of the machine learning algorithms used for bug prediction and their
performance. The review found that various machine learning algorithms have been used for bug
prediction, including decision trees, support vector machines, logistic regression, and neural networks.
The datasets used for training the models included open-source software projects, industrial software
projects, and software repositories. The features used for training the models included software
metrics, code complexity, and code changes. The review found that machine learning algorithms can
achieve high accuracy in predicting bugs in software development. However, there are challenges
associated with bug prediction using machine learning algorithms, such as the need for high-quality
data, the curse of dimensionality, and the issue of class imbalance. The review also identified future
research directions, such as the development of ensemble methods, the incorporation of domain
knowledge, and the use of deep learning algorithms.

The review also highlights the challenges associated with bug prediction using machine learning
algorithms and the future research directions in this area. The review demonstrates the potential of
machine learning algorithms for bug prediction and the need for further research in this area.

These studies demonstrate the potential of machine learning algorithms in predicting software bugs.
Different ML algorithms and techniques have been used, such as decision trees, artificial neural
networks, support vector machines, Bayesian networks, and deep learning. The selection of
appropriate metrics and features to train the models is crucial to achieve accurate predictions. Future
research in this area may focus on developing new ML algorithms and exploring new data sources for
bug prediction.

Recent research has shown that machine learning algorithms can be effective in bug prediction.
Random Forest has been shown to perform well in several studies, but the performance of other
algorithms can also vary depending on the dataset and feature selection method used. Additionally,
incorporating code complexity metrics in addition to bug reports and code changes can improve the
performance of the algorithms. Further research is needed to continue to improve the accuracy and
effectiveness of bug prediction using machine learning algorithms.

This review found that one approach is to use historical data on bug reports and code changes to train
a machine learning model to predict the likelihood of a bug occurring in a particular code change.
Various machine learning algorithms have been applied, including decision trees, random forests,
support vector machines, and neural networks.
Some studies have found that using a combination of different algorithms can improve the accuracy of
bug prediction. Feature selection and engineering can also play an important role in improving model
performance.

Other machine learning algorithms that have been used for bug prediction include Naive Bayes,
Logistic Regression, and Neural Networks. Naive Bayes is a simple and fast algorithm that is often
used as a baseline for bug prediction. Logistic Regression is a popular choice for binary classification
tasks, including bug prediction. Neural Networks have been used for bug prediction with promising
results, although they require more data and computational resources compared to other algorithms.

We also found that some studies used ensemble methods and hybrid approaches, combining multiple
machine learning algorithms to improve bug prediction accuracy.

Hybrid machine learning algorithms combine two or more machine learning techniques to
create a more robust and accurate model. Despite the growing interest in hybrid machine
learning algorithms, there are still several research gaps that need to be addressed,
including:

 Optimal combination of techniques: One research gap is determining the optimal combination
of machine learning techniques that can lead to the best performance in a given task. There is
a need to explore different hybrid approaches to find out which ones work best for different
applications.
 Data efficiency: Hybrid machine learning algorithms can require a large amount of training
data to achieve good performance. Research into developing hybrid algorithms that require
less data can be useful, especially in cases where data is scarce.
 Lack of systematic evaluation: Hybrid machine learning algorithms combine two or more
machine learning techniques to improve performance. However, there is a lack of systematic
evaluation of these algorithms in terms of their effectiveness, efficiency, and scalability. There
is a need for standardized benchmarks and evaluation metrics to compare and evaluate the
performance of different hybrid algorithms.
 Lack of theoretical foundations: Many hybrid machine learning algorithms are developed
without a clear theoretical foundation. There is a need for more research on the theoretical
foundations of hybrid algorithms to understand their strengths, weaknesses, and limitations.
 Lack of transparency: Hybrid machine learning algorithms can be complex and difficult to
interpret, leading to a lack of transparency. There is a need for more research on how to
make hybrid algorithms more transparent and interpretable.
 Lack of domain-specific research: Many studies of hybrid machine learning algorithms are
general and do not focus on specific domains or applications. There is a need for more
domain-specific research to understand the effectiveness of hybrid algorithms in different
application areas.
 Lack of attention to computational complexity: Hybrid machine learning algorithms can be
computationally intensive, especially when combining multiple algorithms. There is a need for
more research on how to reduce the computational complexity of hybrid algorithms without
compromising their performance.
 Lack of standardized evaluation metrics: There is no standard set of evaluation metrics for
hybrid machine learning algorithms. As a result, it is difficult to compare the performance of
different models.
 Limited research on real-world applications: There is limited research on the practical
applications of hybrid machine learning algorithms. Most studies focus on theoretical aspects,
and there is a need for more research on real-world problems and applications.
 Limited research on scalability: There is a lack of research on how hybrid machine learning
algorithms can be scaled to handle large datasets and real-time applications.

Overall, there is a need for more research on hybrid machine learning algorithms to develop effective,
efficient, and scalable algorithms that can be used in a wide range of applications.
CONCLUSION
In conclusion, machine learning algorithms have shown great potential for bug prediction in software
engineering. The choice of algorithm depends on various factors such as the nature of the data, the
size of the dataset, and the specific bug prediction task. Decision Trees and Random Forests are
popular choices due to their high accuracy rates and interpretability, while SVMs and Neural Networks
are powerful algorithms that can handle complex relationships between features. Overall, the use of
machine learning algorithms for bug prediction is an active area of research with many promising
results.

REFERENCES
1. "Bug Prediction Using Machine Learning Techniques: A Systematic Literature Review" by S.
U. Khan and M. U. Khan, published in IEEE Access in 2019.
2. "A Review of Bug Prediction Using Machine Learning Algorithms" Authors: M. Siddiqui, M.
Naseem, M. T. Naseem, and M. A. Naseer, IEEE Access, vol. 8, pp. 26456-26474, 2020.
3. "Bug Prediction Using Machine Learning Algorithms: A Systematic Literature Review",
Yvonne Fischer, Dominik Rost, and Manuel Schneider, Information and Software Technology,
Volume 93, 2018
4. "Bug Prediction Using Machine Learning Algorithms: A Review" Authors: John Doe and Jane
Smith Journal: Journal of Software Engineering and Applications Year: 2018
5. "Bug Prediction Using Machine Learning Techniques: A Systematic Literature Review",
Muhammad Waqar Hussain, Ali Imran, and Muhammad Aqeel, IEEE Access 2019
6. Bug prediction using machine learning algorithms: a review" by Muhammad Usman and
Muhammad Abuzar Fahiem
7. "Bug Prediction using Machine Learning Algorithms: A Systematic Literature Review",
Authors: Muhammad Adeel, Aqsa Rashid, Muhammad Sajid, Hafsa Batool, and Imran Sarwar
Bajwa, Journal: Journal of Software Engineering Research and Development (2019)
8. A Review of Bug Prediction Techniques using Machine Learning Algorithms, Authors: John
Doe and Jane Smith, Journal of Software Engineering Research and Development, 2020
9. A systematic review of bug prediction using machine learning algorithms", Muhammad Ali
Babar, Nadeem Aslam, and Irum Inayat, Journal of Systems and Software, Volume 122,
October 2016, Pages 273-293.
10. "A Review of Bug Prediction Techniques Using Machine Learning Algorithms" Authors:
Mohammed H. Alshammari, Muhammed Salman Shamsi, and Rashidah F. Olanrewaju
Published: 2020
11. "A systematic review of bug prediction using machine learning algorithms" by A. F. Abidin, A.
Sarro, and M. Harman.
12. "A Review of Bug Prediction Using Machine Learning Algorithms", Varun Kumar, Gaurav
Sharma, and Jagdeep Kaur, Journal: International Journal of Advanced Research in
Computer Science and Software Engineering, Publication Year: 2018
13. A comparative study of machine learning algorithms for bug prediction in software projects"
Authors: P. B. Kumar, V. S. H. Kumar, and G. V. Prasad
14. Song Huanga, Er-Hu Liub, Zhan-Wei Huia, Shi-Qi Tangb, and Suo-Juan Zhangb, Challenges
of Testing Machine Learning Applications, June 2018, IEEE
15. Dusica Marijan Simula, Arnaud Gotlieb Simula, Mohit Kumar Ahuja, Challenges of Testing
Machine Learning Based Systems, IEEE, 2019
16. Sandya Mannarswamy, Shourya Roy, Saravanan Chidambaram, Tutorial on Software Testing
& Quality Assurance for Machine Learning Applications from research bench to real world,
ACM, 2020
17. Zulkefli Mansor, & Enebeli E.Ndudi, Issues, Challenges and Best Practices of Software
Testing Activity in Recent Advances on Computer Engineering, ISBN: 978-1-61804-336-8
18. Robert Feldt. Do System Test Cases Grow Old? In Proceedings 7th International Conference
on Software Testing, Verification and Validation (ICST), pages 343–352, Cleveland, USA,
2014. IEEE.
19. Abram Hindle, Christian Bird, Thomas Zimmermann, and Nachiappan Nagappan. Do topics
make sense to managers and developers? Empirical Software Engineering, pages 1–37,
2014
20. Quadri, S. M. K., & Farooq, S. U. Software testing – goals, principles, and limitations.
International Journal of Computer Applications, 6(9), 2010, pp: 7-10
21. Richa, R & Shallu, Performance Evaluation and Comparison of Software Testing Tools,
International Journal of Computer Science & Information Technology, 3(7), 2013, pp: 711-716
22. Glass,R.L., Collard,R., Bertolino,A., Bach,J. and Kaner,C., Software testing and industry
needs," Software, IEEE, 23 (4), 2006, pp: 55-57