Software Defect Prediction Using Integrated Logistic Regression and
Fractional Chaotic Grey Wolf Optimizer
Raja Oueslati
and Ghaith Manita
Laboratory MARS, LR17ES05, ISITCom, Sousse University, Sousse, Tunisia
ESEN, Manouba University, Manouba, Tunisia
Software Defect Prediction, Metaheuristic Algorithm, Grey Wolf Optimizer, Fractional Chaotic,
Logistic Regression.
Software Defect Prediction (SDP) is critical for enhancing the reliability and efficiency of software devel-
opment processes. This study introduces a novel approach, integrating Logistic Regression (LR) with the
Fractional Chaotic Grey Wolf Optimizer (FCGWO), to address the challenges in SDP. This integration’s pri-
mary objective is to overcome LR’s limitations, particularly in handling complex, high-dimensional datasets
and mitigating overfitting. FCGWO, inspired by the social and hunting behaviours of grey wolves, coupled
with the dynamism of Fractional Chaotic maps, offers an advanced optimization technique. It refines LR’s
parameter tuning, enabling it to navigate intricate data landscapes more effectively. The methodology in-
volved applying the LR-FCGWO model to various SDP datasets, focusing on optimizing the LR parameters
for enhanced prediction accuracy. The results demonstrate a significant improvement in defect prediction
performance, with the LR-FCGWO model outperforming traditional LR models in accuracy and robustness.
The study concludes that integrating LR and FCGWO presents a promising advance in SDP, offering a more
reliable, efficient, and accurate approach for predicting software defects.
Software defect prediction (SDP) (Roman et al.,
2023) plays a pivotal role in enhancing the reliabil-
ity and efficiency of software development processes.
By anticipating potential defects early in the software
lifecycle, SDP aids in allocating resources effectively
and mitigating risks associated with software failures.
Despite its significance, accurately predicting soft-
ware defects remains challenging, primarily due to
the complex and dynamic nature of software devel-
opment environments.
Among the array of machine learning (ML) tech-
niques, logistic regression (Acito, 2023) stands out as
one of the most utilized algorithms in SDP. Its pop-
ularity stems from its simplicity and interpretability.
Logistic regression is particularly adept at handling
binary classification problems, like distinguishing be-
tween defect-prone and defect-free software modules.
It estimates the probabilities of classes, providing
clear insights into the likelihood of defects. This as-
pect is invaluable in SDP, where understanding the
risk of defects is as crucial as identifying their pres-
Logistic regression models typically rely on gradi-
ent descent for parameter optimization (Manita et al.,
2023). Gradient descent is a widely used approach
for minimizing the cost function, guiding the model
towards the most accurate predictions. However, this
method has limitations, especially in navigating com-
plex, multidimensional landscapes typical in SDP.
Gradient descent can get trapped in local minima, par-
ticularly with non-convex error surfaces, leading to
suboptimal parameter values and, consequently, less
accurate defect predictions.
Metaheuristic algorithms (Talbi, 2009) emerge as
a potent alternative to gradient descent for parame-
ter optimization in logistic regression models. Un-
like gradient descent, which incrementally adjusts pa-
rameters based on the local gradient, metaheuristics
explore the parameter space more broadly and cre-
atively. For instance, algorithms such as Genetic Al-
gorithm (GA) (Holland, 1992), Particle Swarm Op-
timization (PSO) (Eberhart and Kennedy, 1995), or
Grey Wolf Optimizer (GWO) (Mirjalili et al., 2014)
simulate natural processes to search for optimal or
near-optimal solutions. These methods excel in find-
ing global optima in complex, irregular search spaces
Oueslati, R. and Manita, G.
Software Defect Prediction Using Integrated Logistic Regression and Fractional Chaotic Grey Wolf Optimizer.
DOI: 10.5220/0012704600003687
Paper published under CC license (CC BY-NC-ND 4.0)
In Proceedings of the 19th International Conference on Evaluation of Novel Approaches to Software Engineering (ENASE 2024), pages 633-640
ISBN: 978-989-758-696-5; ISSN: 2184-4895
Proceedings Copyright © 2024 by SCITEPRESS Science and Technology Publications, Lda.
by balancing exploration (searching new areas) and
exploitation (refining known good areas).
By employing metaheuristic algorithms for pa-
rameter optimization, logistic regression models in
SDP can overcome some of the inherent limita-
tions of gradient descent. These algorithms en-
hance the model’s ability to navigate complex, high-
dimensional data landscapes, efficiently identifying
parameter combinations that yield the most accurate
predictions. This approach is particularly valuable in
handling the intricate and often non-linear relation-
ships present in software defect datasets, thereby im-
proving the robustness and reliability of SDP models.
To address these limitations, integrating meta-
heuristic algorithms like GA, PSO, and GWO has be-
come increasingly popular. These algorithms excel
in optimizing ML model parameters and feature se-
lection (Manita et al., 2012; Johnson et al., 2014;
Qasim and Algamal, 2018; Panda and Azar, 2021).
They enhance model performance, reduce complex-
ity, and ensure models effectively handle imbalanced
datasets, which is crucial in SDP scenarios.
This synergy between ML and metaheuristic op-
timization offers an advanced approach to SDP. By
combining the predictive capabilities of ML with
the optimization strengths of metaheuristics, the field
moves towards more accurate, efficient, and reli-
able defect prediction models. The LR-FCGWO ap-
proach is poised to offer significant advantages in
SDP. By harnessing the enhanced search capabilities
of Fractional Chaotic maps, LR-FCGWO can outper-
form traditional algorithms in terms of accuracy and
computational efficiency. This approach is particu-
larly promising in addressing the challenges of high-
dimensional data and complex feature spaces com-
monly encountered in SDP.
This paper aims to explore the application of the
LR-FCGWO approach in the context of SDP. We seek
to investigate its prediction accuracy and computa-
tional efficiency performance, comparing it with ex-
isting metaheuristic algorithms. The remainder of this
paper is structured as follows. In the second sec-
tion, we present an overview of related work. In the
third section, we introduce the GWO algorithm. In
the fourth section, we define the proposed approach
FCGWO. In the fifth section, we presents the LR-
FCGWO to predict software defects. In the sixth, we
evaluate the performance on the proposed approach
on different datasets. We conclude with a conclusion
and some future work.
In recent studies, metaheuristic optimization tech-
niques like Particle Swarm Optimization (PSO) have
been effectively applied to software defect prediction.
(Buchari et al., 2018) utilized Chaotic Gaussian PSO
(CGPSO) on 11 public benchmark datasets, show-
ing improved accuracy in the majority of cases. (Jin
and Jin, 2015) combined Quantum PSO (QPSO) with
a hybrid Artificial Neural Network (ANN) for pre-
dicting defects, demonstrating its efficacy in correlat-
ing software metrics with fault-proneness, thus poten-
tially reducing maintenance costs and enhancing de-
velopment efficiency. Furthermore, (Wahono et al.,
2014) applied Genetic Algorithms and PSO for fea-
ture selection, alongside bagging to address class im-
balance, significantly enhancing prediction accuracy
over traditional methods.
In (Panda and Azar, 2021), the Grey Wolf Opti-
mizer (GWO) is used for feature selection to enhance
bug detection classifiers. This method, applied to
multiclass bug severity classification using MLP, LR,
and RF techniques, demonstrates improved prediction
accuracy on the Ant 1.7 and Tomcat datasets. The
study highlights the potential of GWO in advancing
software quality by efficiently identifying and classi-
fying bugs.
The authors in (Kang et al., 2021) demonstrate the
effectiveness of Harmony Search (HS) optimization
in Just-in-Time software defect prediction (JIT-SDP),
particularly for safety-critical maritime software. By
optimizing model parameters with HS, their approach
surpasses traditional models in predicting defects at
the commit level, showing improved accuracy and
recall rates across various datasets. This highlights
the potential of HS in enhancing software quality as-
surance through better management of class imbal-
ances and prediction performance. As reported in
(Elsabagh et al., 2020), the authors employ the Spot-
ted Hyena Optimizer algorithm with a multi-objective
fitness function for defect prediction in cross-project
scenarios. This approach aims to improve classifi-
cation accuracy where historical data is sparse and
varied. Testing on NASA datasets—JM1, KC1,
and KC2—the algorithm outperforms traditional data
mining techniques, achieving accuracy rates of 84.6%
for JM1, 92.0% for KC1, and 82.4% for KC2, high-
lighting its effectiveness in defect prediction.
In (Niu et al., 2018), the study introduces an
adaptive, multi-objective Cuckoo Search algorithm
aimed at enhancing defect prediction accuracy by op-
timizing multiple objectives and adaptively adjust-
ing dataset ratios to balance module sizes. This ap-
proach outperforms traditional defect prediction mod-
ENASE 2024 - 19th International Conference on Evaluation of Novel Approaches to Software Engineering
els, with simulations confirming its superior accuracy
in defect prediction. The authors in (Zhu et al., 2021)
introduce EMWS, a feature selection algorithm com-
bining Whale Optimization Algorithm (WOA) and
Simulated Annealing (SA) for software defect pre-
diction. This method significantly improves predic-
tion accuracy by effectively reducing irrelevant and
redundant features, showcasing EMWS’s efficacy in
enhancing model performance.
As reported in (Zivkovic et al., 2023), the meta-
heuristic employed is a modified variant of the rep-
tile search optimization algorithm, HARSA (Hyper-
parameter Adjustment using Reptile Search Algo-
rithm). The results show its ability to enhance the
performance of the defect prediction model. In (Balo-
gun et al., 2020), metaheuristic search methods used
in this study for SDP in the context of Wrapper Fea-
ture Selection (WFS) include 11 state-of-the-art meta-
heuristics and two conventional search methods. The
goal is to compare the effectiveness of these meth-
ods in addressing the high dimensionality problem
and improving the predictive capabilities of models
in SDP.
The study (Raheem et al., 2020) investigate the
effectiveness of the Firefly Algorithm (FA) and Wolf
Search Algorithm (WSA) in feature selection, com-
bined with Support Vector Machine (SVM) and Ran-
dom Forest (RF) classifiers for software bug predic-
tion. They use public software module datasets to
compare the performance of these machine learning
techniques. The authors in (Goyal and Bhatia, 2021)
explore the Lion Optimization-based Feature Selec-
tion (LiOpFS) method, which excels in choosing an
optimal feature subset from high-dimensional data,
tackling the Curse of Dimensionality. By filtering out
only the most relevant features, LiOpFS significantly
enhances classifier performance and fault prediction
model accuracy.
The Grey Wolf Optimizer (GWO), as introduced in
(Mirjalili et al., 2014), stands out in the metaheuristic
landscape for its unique emulation of the social hi-
erarchy and hunting behaviour of grey wolves. This
algorithm encapsulates the wolves’ strategy of track-
ing, encircling, and ultimately attacking the prey. The
GWO algorithm is mainly characterized by incor-
porating the leadership and collaborative dynamics
within a wolf pack.
The mathematical model of the GWO initiates
with the encircling behaviour, described by:
D = |
C ×
X(t)| (1)
X(t + 1) =
A ×
D (2)
(t) denotes the prey’s position vector,
X(t) represents the position vector of a wolf, and t
signifies the current iteration. The coefficients
A and
C are computed as:
A = 2 ×a ×r
a (3)
C = 2 ×r
wherea linearly decreases from 2 to 0 as iterations
progress, andr
are random vectors within the
range [0, 1]. The social hierarchy within the wolf
pack is such that the alpha (α) wolfs position is con-
sidered the current best solution. The beta (β) and
delta (δ) wolves follow the alpha, and the rest of the
pack (omega wolves) follow these leading members.
The phase of attacking the prey, the exploitation
phase, is marked by a reduction in the magnitude of a,
allowing the pack to converge towards the prey’s posi-
tion. This interplay between exploration (high a) and
exploitation (low a) is a testament to the algorithm’s
capacity to explore and exploit the search space adap-
tively, propelling it towards the optimal solution with
an effective balance. Figure 1 depicts the flowchart of
the original GWO algorithm.
Figure 1: GWO Algorithm: A Detailed Flowchart.
Achieving a delicate balance between exploration and
exploitation is key in optimization algorithms to effi-
ciently navigate complex search spaces. The Frac-
tional Chaotic Grey Wolf Optimizer (FCGWO) inno-
vatively combines fractional order chaos maps with
Software Defect Prediction Using Integrated Logistic Regression and Fractional Chaotic Grey Wolf Optimizer
the traditional GWO to balance exploration and ex-
ploitation in complex search spaces. This approach
uses the complex dynamics of chaos maps to evade
local optima, enhancing search efficiency. The fol-
lowing discussion will detail the mechanisms of frac-
tional chaos maps and FCGWO, illustrating its poten-
tial in solving various optimization challenges.
4.1 Fractional Chaotic
This research integrates three advanced fractional or-
der chaos maps (Wu and Baleanu, 2014; Wu et al.,
2014; ATALI et al., 2021) into the Grey Wolf Op-
timizer (GWO) to boost its performance by ad-
dressing local optima entrapment and optimizing the
exploration-exploitation balance, essential for reach-
ing optimal solutions.
Fractional Logistic Map. Renowned for its in-
tricate dynamical properties, the fractional logis-
tic map is instrumental in enriching the diversity
within the search space, thereby facilitating more
extensive exploration.
Fractional Sine Map. The map’s inherent peri-
odic nature is crucial in ensuring a comprehen-
sive scan of the solution space, enhancing the al-
gorithm’s thoroughness.
Fractional Tent Map. This map introduces a
unique approach to traversing the optimization
landscape, offering novel pathways through the
search space.
The integration of these maps (detailed in Table
1) is an addition to the GWO algorithm and a strate-
gic incorporation of complex dynamical systems the-
ory into optimization. By leveraging the intricate be-
haviours of these chaotic systems, we aim to boost
the GWO algorithm’s ability to navigate and exploit
multidimensional search spaces effectively, leading to
more accurate, reliable, and quicker convergence to
optimal solutions across various scenarios.
Table 1: Fractional chaos maps with range 0 and 1.
Name Equation x(0) Parameters
Fractional Logistic x
= x
γ(k j+α)
γ(k j+1)
(1 x
) 0.3 µ = 2.5, α = 0.3
Fractional Sine x
= x
γ(k j+α)
γ(k j+1)
) 0.3 µ = 3.8, α = 0.8
Fractional Tent x
= x
0.3 µ = 1.9, α = 0.6
Γ(k j+α)
Γ(k j+1)
min((µ 1)x
,µ (µ + 1)x
4.2 Mechanistic Foundations of
The FCGWO builds upon the traditional GWO by re-
placing the random number generation process with
a sequence derived from fractional chaotic maps. In
FCGWO, the chaotic sequence is used to update the
position and velocity of the search agents (i.e., the
grey wolves) in the search space. The following out-
lines the key steps of the FCGWO algorithm:
1. Initialize the grey wolf population, along with
their positions and velocities.
2. Evaluate the fitness of each grey wolf.
3. Calculate the social ranks (i.e., alpha, beta, and
delta wolves) based on the fitness values.
4. Update the positions and velocities of the grey
wolves using the standard update rules (i.e., Equa-
tions (1) and (2)). However, the calculation of the
A and
C are updated using Fractional
Chaotic maps instead of random values following
the standard behavior. Then, the coefficients
C are now computed as:
A = 2 ×a ×
ψ(t) a (5)
C = 2 ×
ψ(t) (6)
5. Repeat steps 2-4 until a termination criterion is
met (e.g., maximum number of iterations, suffi-
cient solution quality).
The introduction of the fractional chaotic sequence
ψ(t) in the FCGWO update rules fosters exploration
and diversification, enabling the algorithm to avoid
local optima and efficiently search complex problem
landscapes. The following sections will delve deeper
into the potential applications and performance eval-
uation of the FCGWO algorithm. Figure 2 depicts the
flowchart of the proposed FC-GWO algorithm.
Figure 2: Flowchart of FCGWO Algorithm.
The integration of LR with the FCGWO offers a
groundbreaking approach to SDP. LR, a standard
ENASE 2024 - 19th International Conference on Evaluation of Novel Approaches to Software Engineering
method for binary classification, is especially use-
ful in SDP for its simplicity and interpretability. It
calculates the probability of a software module be-
ing defect-prone, a crucial aspect of software qual-
ity assurance. However, LR faces challenges such as
overfitting and inefficiency in high-dimensional data
The FCGWO, inspired by the social and hunt-
ing behaviours of grey wolves and enhanced with
the dynamic capabilities of Fractional Chaotic maps,
is employed to address these limitations. This ad-
vanced metaheuristic algorithm optimizes LR param-
eters, aiding in tackling complex, high-dimensional
datasets prevalent in SDP. Traditional parameter tun-
ing methods often must catch up in such scenarios,
leading to overfitting or inadequate feature selection.
In contrast, FCGWO’s adaptive nature allows for a
more refined and practical approach.
An added benefit of FCGWO is incorporating
Fractional Chaotic maps, which boosts the algo-
rithm’s ability to escape local optima. This feature
is particularly valuable in handling the non-linear re-
lationships characteristic of SDP data, which standard
LR models may not effectively capture.
5.1 Objective Function
As previously mentioned, we employ the FCGWO al-
gorithm as a training mechanism for the logistic re-
gression model. Specifically, each potential solution,
comprising a set of weights and biases produced by
FCGWO, is evaluated using the LR model. The per-
formance of each solution ( f
) is then quantified using
the discrepancy (E
) between the predicted (p) and ac-
tual (y) outputs, as depicted in the equations below:
1 + E
where n is the number of rows used in the training set.
5.2 Data Preprocessing
For our work, we opted for the Min-Max method (Is-
lam et al., 2022), which simplifies value comparisons.
Following normalization, we applied a data transfor-
mation method, which is an essential part of the ini-
tial data preparation before statistical analysis. This
method also facilitates comparison and interpretation.
Moreover, true (yes) and false (no) values were trans-
formed into 1s and 0s, respectively. We also em-
ployed cross-validation, a data resampling technique,
to assess the generalization ability of predictive mod-
els and prevent overfitting (Berrar, 2019).
5.3 LR-FCGWO Steps
Figure 3: Flowchart of the LR-FCGWO Model for Software
Defect Prediction.
This section presents an overview of the LR-FCGWO
model’s workflow, illustrated in Figure 3. This work-
flow is designed to systematically process software
metrics, apply optimization techniques, and utilize
predictive analytics to identify potential defects.
1. Loading Data. The process begins by importing
the relevant datasets to be used for software defect
2. Preprocessing. Subsequently, data undergoes
preprocessing to confirm it is ready for analysis.
This includes data cleaning, normalization, and
transformation procedures.
3. SMOTE. To counteract class imbalance within
the datasets, the Synthetic Minority Over-
sampling Technique (SMOTE) (Chawla et al.,
2002) is employed, which helps prevent model
bias towards the majority class.
4. 10 k-fold Cross Validation. Data is parti-
tioned into ten segments, and the model is cross-
validated to ensure robustness and to reduce the
risk of overfitting. This iterative cross-validation
is indicated by the decision diamond ’K¡=10’.
5. Training Set. The LR-FCGWO model is then
constructed using the training set obtained from
the cross-validation process.
6. Test Set. The model, now trained, is applied to
the test set to perform defect prediction.
7. Classification Results. Outcomes of the classifi-
cation are compiled to reflect the model’s perfor-
mance, as judged by metrics like accuracy, preci-
sion, and recall.
8. Display Results. The results from the classifica-
tion process are then displayed.
Software Defect Prediction Using Integrated Logistic Regression and Fractional Chaotic Grey Wolf Optimizer
6.1 Datasets
In this study, five datasets from the PROMISE
REPOSITORY (Sayyad Shirabad and Menzies, 2005)
are selected, as shown in Table 2, varying in terms of
the number of lines and defect rates.
Table 2: Description of the Datasets.
Number of Yes No Number of Number of Missing
Attributes Instances Values
CM1 22 49 449 498 0
JM1 22 8779 2106 10885 0
KC1 22 326 1783 2109 0
KC2 22 105 415 522 0
PC1 22 1032 77 1109 0
6.2 Results and Discussions
In this section, all experiments were repeated 51 times
independently to obtain statistically significant re-
sults, implemented using MATLAB R2020a.
The comparative analysis encompassed in the pre-
sented tables evaluates a range of machine learning
models: Logistic Regression (LR-standard), Random
Forest (Breiman, 2001), Gradient Boosting (Natekin
and Knoll, 2013), AdaBoost (Freund and Schapire,
1997), Support Vector Machine (SVM) (Kramer
and Kramer, 2013), K-Nearest Neighbors (K-NN)
(Kramer and Kramer, 2013), Decision Tree (Rokach
and Maimon, 2005), XGBoost (Chen and Guestrin,
2016), and Logistic Regression with CFGWO (LR-
CFGWO). These models are assessed across various
datasets, namely CM1, JM1, KC1, KC2, and PC1,
focusing on four key performance metrics: Accuracy,
Precision, Recall, and F1 Score.
In the CM1 dataset, LR-CFGWO stands out as the
most proficient model, achieving the highest scores
across all metrics (Table 3). This model’s superior
performance is evident in its exceptional balance of
precision and recall, leading to a robust F1 Score.
Similarly, in the JM1 dataset, the Random Forest
model exhibits outstanding performance, marking the
highest scores in all evaluated metrics (Table 4). This
fact indicates its effectiveness in handling this dataset,
especially regarding accuracy and precision.
For the KC1 dataset, LR-CFGWO again shows
remarkable performance, topping the charts in all
metrics (Table 5). This consistency underscores the
model’s robustness and adaptability across different
datasets. In the analysis of the KC2 dataset, LR-
CFGWO and Random Forest models show compet-
itive performance. However, LR-CFGWO slightly
edges out with superior accuracy and precision, while
Table 3: Comparative Performance Analysis of Various Ma-
chine Learning Models on the CM1 Dataset.
Model Accuracy Precision Recall F1 Score
LR-standard 80.735 80.396 81.292 80.842
Random Forest 92.428 89.278 96.437 92.719
Gradient Boosting 90.646 86.573 96.214 91.139
AdaBoost 86.971 82.937 93.096 87.723
SVM 81.180 79.046 84.855 81.847
K-NN 83.519 75.904 98.218 85.631
Decision Tree 85.635 82.787 89.978 86.233
XGBoost 92.094 88.730 96.437 92.423
LR-CFGWO 92.984 89.549 97.327 93.276
Table 4: Comparative Performance Analysis of Various Ma-
chine Learning Models on the JM1 Dataset.
Model Accuracy Precision Recall F1 Score
LR-Standard 66.751 69.031 60.760 64.632
Random Forest 91.703 91.546 91.893 91.719
Gradient Boosting 81.957 84.140 78.759 81.361
AdaBoost 73.782 73.991 73.345 73.667
SVM 67.336 71.100 58.418 64.138
K-NN 81.826 75.132 95.145 83.962
Decision Tree 86.560 86.752 86.299 86.525
XGBoost 88.510 92.318 84.010 87.969
LR-CFGWO 87.875 92.210 82.740 87.219
Table 5: Comparative Performance Analysis of Various Ma-
chine Learning Models on the KC1 Dataset.
Model Accuracy Precision Recall F1 Score
LR-Standard 71.901 71.682 72.406 72.042
Random Forest 88.923 85.626 93.550 89.413
Gradient Boosting 84.773 83.120 87.269 85.144
AdaBoost 78.435 74.877 85.586 79.874
SVM 73.472 70.998 79.361 74.947
K-NN 81.716 75.167 94.728 83.821
Decision Tree 83.707 81.833 86.652 84.173
XGBoost 88.839 87.251 90.970 89.072
LR-CFGWO 89.484 88.344 90.970 89.638
Table 6: Comparative Performance Analysis of Various Ma-
chine Learning Models on the KC2 Dataset.
Model Accuracy Precision Recall F1 Score
LR-Standard 78.795 78.935 78.554 78.744
Random Forest 88.675 85.275 93.494 89.195
Gradient Boosting 87.711 85.812 90.361 88.028
AdaBoost 82.892 82.578 83.373 82.974
SVM 78.675 77.674 80.482 79.053
K-NN 81.084 76.113 90.602 82.728
Decision Tree 86.265 86.618 85.783 86.199
XGBoost 87.711 85.488 90.843 88.084
LR-CFGWO 88.916 87.298 91.084 89.151
Table 7: Comparative Performance Analysis of Various Ma-
chine Learning Models on the PC1 Dataset.
Model Accuracy Precision Recall F1 Score
LR-standard 80.814 79.336 83.333 81.285
Random Forest 95.736 94.444 97.190 95.798
Gradient Boosting 93.750 91.536 96.415 93.912
AdaBoost 89.874 87.240 93.411 90.220
SVM 83.285 77.769 93.217 84.795
K-NN 90.843 85.037 99.128 91.544
Decision Tree 91.812 90.065 93.992 91.987
XGBoost 95.785 94.787 96.899 95.831
LR-CFGWO 96.124 95.161 97.190 96.165
Random Forest excels in recall (Table 6), indicating
its strength in identifying relevant instances.
ENASE 2024 - 19th International Conference on Evaluation of Novel Approaches to Software Engineering
Figure 4: ROC Curve Analysis of Classifier Performance
on KC2 Dataset.
In the PC1 dataset, LR-CFGWO again demon-
strates its dominance, leading in accuracy, precision,
and F1 Score (Table 7). The model’s consistent per-
formance across different datasets highlights its effec-
tiveness and reliability in various contexts.
After that, we present the Receiver Operating
Characteristic (ROC) curves for different classifiers
applied to KC2 dataset. ROC curves are a graphical
representation that illustrate the diagnostic ability of
binary classifiers as their discrimination threshold is
varied. The area under the ROC curve (AUC) pro-
vides a single measure of overall performance of a
classifier. The closer the ROC curve is to the top-left
corner, the higher the overall accuracy of the test.
In Figure 4, the ROC curve for the KC2 dataset in-
dicates that the LR-FCGWO classifier achieves a re-
markable AUC of 0.99, paralleling the excellent per-
formance of the Gradient Boosting classifier. The
high AUC value underscores the LR-FCGWO clas-
sifier’s exceptional proficiency in discriminating be-
tween the classes.
Overall, the analysis reveals that while different
models have their strengths in specific datasets, LR-
CFGWO consistently emerges as a top performer,
showcasing its versatility and effectiveness in various
predictive tasks.
The LR-FCGWO methodology introduces an innova-
tive approach to Software Defect Prediction (SDP),
combining the predictive capabilities of Logistic Re-
gression with the optimization strengths of the Frac-
tional Chaotic Grey Wolf Optimizer (FCGWO). This
method enhances defect prediction by optimizing
model parameters, effectively addressing overfitting
and the challenges posed by high-dimensional data.
It leads to an improved and more detailed predictive
model for detecting software defects.
The potential avenues for further research and
application of the LR-FCGWO model are manifold
and promising. One prospective area of exploration
is the continued refinement and optimization of the
FCGWO algorithm itself. Enhancing algorithmic effi-
ciency or adaptability could further elevate the perfor-
mance of LR-FCGWO, especially in scenarios char-
acterized by exceedingly large or complex datasets.
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