Performance of Various Computational Intelligence Methods in Software Defect Prediction: An Analytical Perspective
K. Eswara Rao*
1, G. Appa Rao
2, S.Anuradha
31Dept. of CSE, Aditya Institute of Technology and Management, Tekkali, AP,INIDA-532201.
23 Dept. of CSE, GITAM (Deemed to be University), Visakhapatnam, AP, INDIA-530045.
E-mail: 1[email protected]2[email protected]
Abstract
Success of any IT industry is depends on how much Quality of Software (QoS)developed; its turn by engineering methodology enables production of QoS, Software defects can influence the quality of software. Probability of defect occurrences can directly affect to QoS. An Software Defect Prediction (SDP) is efficient whenever intelligence algorithm accuracy and fault detection rate is high. In this paper provides a systematic review on SDP by using various computational intelligence methods and most popular methods for fault prediction. When the size of software is huge it becomes challenge to predict the software defect,Six algorithms were analysed using Weka tool apply various intelligence algorithms on JM1 dataset and calculating accuracy and fault prediction rate, and Finally we should understandone classifiers is good in terms of good accuracy of SDP over the other algorithms.
Keywords: SDP, JM1, Computational Algorithms, QoS, Softwaremetrics
1 Introduction
To improve QoS in theprocess of software products and development but overcome thefailure rate and defect density. Approaching SDP to build quality defect models but mustconcentrate on metrics of software measurements of code and data defects.By using theseexpectations to getting of quality of each end product by capturing good software metrics.Based on these knowledge stored in historical data, characterize and representing of softwaremetrics and defects data, then one can easily predict the SQP models.
Mainly this QoS model is developed by using the software metrics and defects by collecting similar software projects or previously developed system releases. Based on the validation of models, we can easily find the software defects of each and every program modules under development. We can decide the applications of QoS improvement resources based on low quality or Defect prediction to those programs. An defect prediction also defends the partial resources towards non-applications for extraordinary feature software package. The main goal is to fulfil the QoS reliability with effective resources. We can categorize the Software System Predictions (SSP) connections into two methods to improve the software defects rate. 1. Static Defect Prediction (STDP). 2. Dynamic Defect Prediction (DDP). Dynamic Prediction is excellent when compared to Static prediction. DDP mainly used in standard models to detect the errors on search based perspective.
By using this search we can easily build the pre-processing steps for SDP models. A feature selection can be classified by feature ranking and subset selection [3]. By go through ranking of features according to the criteria’s the analysts choose some features (software metrics) for a datasets individually. For getting the hybrid search-based we can combine both feature ranking and selection [4][5].
By this approach we can select the suitable metrics and selection of feature. But here SDP has a problem in computer science field. To solve these SDP drawbacks there are many techniques, tools and offer many models offered by the domains are: data mining (DM), fuzzy logic (FL), machine learning (ML) etc. FL has a special role in detecting the early SDP using software metrics [8].
By the achievement of the above, many authors of this paper focus several procedures and different machine learning algorithms for high detection rate and usage of testing and training datasets and for evolution. For training defect predictors we have five different learners [6]:It uses NB Classification (NB), Multilayer Perception (MLP), Support Vector Machine (SVM), Logistics Regression (LR) and K-Nearest Neighbour (KNN). This research has some drawbacks [7] of NASA Promise dataset [8]. The metrics of NASA Data Program [31] is used for various SDP research.
There is no proper classification algorithm on datasets while in study we check the performance of algorithm, but each and every algorithm perform best for some datasets on individual features by using WEKA[1].
This paper turns into five sections in section2 Literature review aim is that which focuses what work has been done on SDP. Section3 shows a view about Software Detect Prediction (SDP).
Section4 tells different classification algorithms under study. Section5 discuss proposed model.
Section 6 benevolences the investigational results and Section7 stretches supposition and upcoming scope.
2 Literature Survey
Research in SDP, the software quality must be best for element to analyse the software quality.
SDP has many intelligence methods, but here some extents were considered for literature review.
In 2018 Z. Li, X. Jing and X. Zhu [9] it have more number of research papers for SDP. The author defines the Software Detection clearly categorized into four ways.in their research initially follows ML approach for prediction, next follows manipulating desired dataset, one moreis effect aware approach, experiential studies, correct direction to study on Software prediction process and software metrics for defect prediction.
By applying ARNN to predict SD was proposed by Fan, Guisheng&Diao in 2019 [10], In their researchextracting vectors from abstract tree program, applying dictionary mapping, word embedding mechanism connect these vectors as an inputs to ARNN.Mechanically it traines syntactic and semantic feature.Finally ARNN to produce weighty features for precise prediction.
A combination of two unique standard techniques KPCA (Kernel Principal Component Analysis) and WELM (Weighted Extreme Learning Machine) for solving the problem of SDP was developed by Xu et al.[11]. The authors named the methodology as KPWE. GRBF has been incorporated into KPCA as function of kernel during the stage of feature extraction. NASA data sets have been utilized for the performance evaluation. The proposed method yields better accuracy for cracking the conflict of SDP.
An empirical analysis on the significance of intelligence algorithms for solving the problem of SDP had made by Yalciner&Ozdes [12]. Standard datasets of NASA were taken from publicly available dataset repository (PROMISE) and were utilized for performance evaluation. Accuracy, precision rate etc were considered as performance metrics and distinct algorithms: MLP, RBF, SVM, Bagging etc were considered for estimating the SD. The authors claimed all the considered techniques yields better rate of accuracy for solving SDP. Moreover, they have also mentioned that bagging technique yields better accuracy among all.
A modern ACAR (Atomic Class Association) method for SDP was made by Shao et al. [13]
Redundant pruning was incorporated into the proposed method and fifteen standard datasets were considered for performance evaluation. A comparison has been made with some state-of-the-art of learners such as SVM, CBA2 (Classification Based on Associations 2), DT (Decision Tree) etc.
consider the terms of balance foundatomic class association yields improved performance ratherthan CBA2.
Arar and Ayan [14] have developed FDNB (Feature Dependent Naive Bayes) methodology with the help of pre-processing to solve the complex problem of SDP. For evaluating the efficacy of the model, standard NASA promise datasets CM1, PC1, KC3 etc has been used. The proposed FDNB has been compared with the benchmark NB method. Based on the experimentations done, the authors claimed their method yields better performance for solving SDP problem rather than the compared one.
3Basic Preliminaries and Background Study of Software Defect Prediction (SDP) We can use well-organized software defect predictor models for and is related to mistake predictions. In this software defect, a defect can be a bug, fault, flaw, malfunction, mistake and an error in the error. It causes an erroneous and unpredicted outcome [15] due to these defects. Due to these faults major system properties and design, manufacture and external environment are appearing.
When compared with anticipation, software flaws effects different programming errors in performance. The major faults occurred from source code or design, many faults are from compilers while code generating is incorrect. These faults are a danger problem for software developers and clients. Defects of Softwarecan’t purely decrease the software quality, it increase the cost but the development schedule is delay. To avoid these sort of trouble the software fault predicting is proposed. SDP progress efficiently because the software testing and direct allocation of the resources and its effectiveness. In the SDLC early phase, the software flaws are detected and corrected to develop software quality.
3.1SDP Process
To build a prediction model, the first step is release instances from the software achieves such as issue tracking systems, e-mail achieves, version control system and so on. According to granularity, the source code file, software component, a class, a method (or function) and a code change to represent a system for each and every instance. For each instances, the software achieves are extracted from metrics and is named with number of bugs or buggy or clean. By using these techniques, to generate these instances with labels and metrics used in machine learning. We can study defect prediction techniques for data normalization, feature selection and noise reduction in a final set of training instances. Figure 1.1 shows the prediction model predicts whether a new instance has a bug or not.
The binary classification for any instance stands for bug proneness prediction, here the number of bugs referred as regression.
3.2 Software Defect Management
The early phase of SDLC main aim is to identify the software quality and finding the defects in software defect management. In SDLC there are main phases, they are requirement gathering, analysis, designing, coding, testing, implementation and maintenance phases. SDLC has one complicated job in the primary phase for identifying defects, so we can choose methods while applying. The main stages in softwaredefect(SD) handling include [16].
SD Identification
SD Categorization
SD Analysing
SD Predicting
SD Removing
3.3 Software Defect Prediction (SDP)
SDP requires a broad range of testing for finding the defective modules. Due to this early identification an error guides to effective resource allocation, time reduce and cost of high quality software cost. Hence, in evaluating, understanding and improve the software quality SDP plays a major role.
3.4 Software Defect Metrics
By using software metrics, we can easily analysis the number of defects in components while investigating the Software Metrics Extensive. To evaluate the progress of the software we use a quantitative measure that software metrics. These are three parameters are used and measured as to describe the following. 1. Process metrics evaluate the efficiency, effectiveness of the defect deduction in development, maturity of the process effort required in the process, importance of the software process and so on. 2. Product metrics in many phases this measurement work product is development of a software development and is created from requirement.
Defect prediction uses many different software metrics. The metrics are:
LOC metric
McCabe’s Complexity
Halstead metrics
McCabe Essential Complexity (MEC) metric
The McCabe module Design Complexity (MMDC) Metric
Object Oriented Metrics
4 Methods of Computer Intelligence
Data Mining has some important role in SDP and many techniques applied to SDP. To resolve the problems in SDP, the ML is identified as a standard methodology for its higher usage levels. The ML has many intelligence algorithms applied in SDP like classification (Naïve Bayes, K Nearest Neighbour), clustering, Decision trees (LR, Random forest), Neural Networks (Artificial Neural Networks) and ELA (Ensemble Learning Algorithms) used for SDP [30]. These proposals suitable for resolving the problems of SDP. The ML has an approval that it shows a great significance in solving SDP problems when equated to state-of-the-art approaches.
4.1 Classification
Classification is a mining intelligence which predicts categorical labels, is a method takes number of instances in a collection simply called dataset considered and assign it to a known model. A classifier classifies data and predicts particular class label normal or abnormal. Classifiers are suitable for best in misuse and little in anomaly detection methods. Datasets are converted into predetermined sets by applying classification. Different classification techniques such as LBk, Naivebayes classifier, J48, Random forest, Hoeffding Tree etc. are used in SDP.
4.1.1 IBk
K-nearest neighbour classifier is IBK [17] an intelligence technique in Weka tool. This intelligence method uses comparable distance metric. IBk can be automaticallyresolute by using LOOC validation accentuate to angreatercertainagreed by significance. To find firm KNN value by following Different searching processes. There are an enormous options available like Trees, Knowledge discovery, ball, cover[19] instead of traditional searching. Distance functions active by parameters and the behind thing is similar as “IBL.” That is a Euclidean distance; further choices are “Chebyshev,” “Manhattan,”
and “Minkowski distances.”To find the measure from one knn to other knn for weight by taking an n number of training samples.Translating distances into weights approached by two dissimilar formulas.
Choose window size as limit and preparetraining examples that are held by the classifier. When novel examples are included, previous examples are deleted to retain the training examples at this size [18].
4.1.2 Hoeffding Tree
Most streaming algorithm suffers with two major problems first one the order in which input appears second slower in batch processing .to overcome these problems in the construction of decision tree using Hoeffding [23] follows incremental fashion and assumes the distribution of data not change over time . and splitting of node is only possible when the sufficient statistical evidence exist with attribute associated with that node. Decision tree learned by Hoeffding is asymptotically identical to non-incremental learner if training data is large enough.
4.1.3 j48 Model
In j48 [25] process is an easy program of a C4.5 DT algorithm for classifying features. By using this generate decision trees (DT). But it most advantagable for handling classification problems. For tree construction build a simple model by using the J48 algorithm. When a DT is build, it is in use to each record of data- set. Missing values is mainly avoided in C4.5 algorithm, from other records what is the known about attributes can be forecasted by item value during tree construction. Core intention is that data is partitioning into range. It be dependent onon value for that entry that is in training example data. C4.5models classifies through “DT” or rules produced from them.
4.1.4 NaiveBayes
The Naive Bayes [24] [27] classifier is a probabilistic classifier based theorem. Naïve Bayes algorithm computes probability of each class and out puts a class with max probability .For the computation complexity Naïve Bayes assumes that all attributes are independent of each other for a given class C1, C2, C3… CN are different class and X is unseen input then the input assigned class label Cj is
P(x/Cj)>P(X/Ci ) for i≠j for i=1,2…….N
4.1.5 Random forest (RF)
RF [27] intelligence algorithm is a supervised algorithm. It follows the idea of construction of decision tree instead it creates a collection of decision trees .It assigns the class label for unseen input based on the class labels obtained from decision trees by taking majority voting.
Algorithm for Random forest
1. Randomly select “t” attributes from total “n” attributes where t<< m 2. Among the “t” attributes, calculate the node “d” using the best split point 3. Split the node into d nodes using the best split
4. Repeat the 1 to 3 steps until “l” number of nodes has been reached
5. Build forest by repeating steps 1 to 5 for “k” number times to create “k” number of trees
4.1.6 Sequential Minimal Optimization(SMO)
SMO [28]isrecycled to train support vector machine. The SMO algorithm contains many optimizations designed to speed up on large datasets.
5 Proposed Framework
Different classification algorithms are focused in the Software Defect Prediction(SDP) to verify the efficiency between the methods. An effective model was implemented step by step shown in Figure1.
In this study we consider JM1 data set which contains samples of different features. In pre-processing phase we sample some data and also prepared data set of individual features from the original dataset.
Figure 1: Experimental evolution
This experimental study focuses on six frequently used best methods named J48, LBK, SMO, Naïve Bayes, Hoeffding tree and Random forest. For performance evaluation this study considers two measures accuracy and Defect prediction rate. Based on performance evolution measures compare the accuracy for all six classifier methods and proposes which is best classifier and also propose which intelligence algorithm is finest one on behalf of SDP, By this learning each classification technique is applied on each category of defect and identify the accuracy and defect rate of each one.
5.1 Discussion and Evaluation
Here the JM1 Dataset has been using for various research works mainly in data mining research.
D
is having 10885 instances and 22 attributes such as five various LOC measure, 3 McCabe metrics, 4 base Halstead measures, 8 derived Halstead measures, 1 branch-count, and one target field. It has no missing attributes. More details about the attributes are represented in Table 1.Table 1. Attribute Information of the JM1 dataset
The dataset considered in this paper is JM1 (software defect prediction) which is publicly available in PROMISE repository [29]. In this proposed algorithm design, the
xicomposed of values from the attribute number 1 to 20 and yi
0,1 composed of target attribute i.e. number 22 (“defects”). For my experimental purpose set of instances from actual JM1 dataset shown in Table2.Data extraction is based on some basis like initially set of instances (Table 2 of each classification model (Table2) done for 10885 instances. Table2 shows Naive Bayes classified8742 instances, SMO classified8788 instances, C4.5classified 9518 instances, RFclassified 10709 instances,ADT classified 8816 instances and Ibk classified 10761 instances. By using these dataset present in Table 2 uses effectively evaluate the accuracy of different intelligence methods.
Table 2: Dataset from JM1
Classification Model
Actual Data for Classification Total No. of
Instances
No. of instances classified
Incorrectly classified Naive Bayes
10885
8742 2136
SMO 8788 2097
C4.5 9518 1367
Random Forest 10709 176
Hoeffding Tree 8816 2069
Ibk 10761 124
5.2 System Setup
Every part of experiment be execute in high build up system with the Intel(R) Core i5 Processor, 3 Gigabytes RAM and an independent system contains windows 7 operation system. For experimental execution purpose takes support of anfree open source software (machine learning) package tool Weka version 3.8. Weka is a software tool contains collection of machine learning models for intelligence algorithms like data pre-processing, classification, clustering, association rules and visualization. All the intelligence methods have been used in this paper are applied in Weka so that everything be easy and every result is fair compared to each other
.
6 Descriptions about the Result
From Table 3 and Figure 2 we conclude that all most all classification performing well on overall data set under the study except naive base algorithm which behaved poorly.
Table 3 Performance Evolution Classification Model Accuracy False Rate
Naive Bayes 80.476 17.27
SMO 80.735 16.26
C4.5 81.474 14.27
Random Forest 92.312 6.97 Hoeffding Tree 91.722 7.96
Ibk 83.098 13.27
Figure2. Performance Evolution
6.1 Analytical Description about the Result
From Table-4 and Figure-3 it is evident that for TP RateHoeffding tree gives classification accuracy of nearly 80% which very high compare to reaming algorithms under the study. From Table-4 and Figure-4 it is evident that for FP RateLBk gives classification accuracy of 0.65 which very high compare to reaming algorithms under the study. From Table-4 and Figure-5 it is evident that for TN Rate and C4.5gives classification of 0.97 which very high compare to reaming algorithms under the study. From Table-4 and Figure-6 it is evident that for ROC Hoeffding tree gives classification accuracy of nearly 86% which very high compare to reaming algorithms under the study
Table 4 Intelligence Evolution for Performance Metrics
Classification Model
Performance Metrics
TP Rate FP Rate TN Rate Precision ROC Naive Bayes 0.167 0.042 0.95 0.486 0.56
SMO 0.198 0.055 0.94 0.463 0.57
C4.5 0.126 0.022 0.97 0.579 0.55
Radom Forest 0.667 0.015 0.98 0.91 0.82 Hoeffding Tree 0.774 0.048 0.95 0.791 0.86
IBk 0.4 0.065 0.93 0.593 0.66
80.476 80.735 81.474
92.312 91.722
83.098
17.27 16.26 14.27 6.97 7.96 13.27 0
20 40 60 80 100
Accuracy False Rate
Figure 3 TP Rate
Figure 4FP Rate
Figure 5TN rate
Figure 6ROC
0.167 0.198 0.126
0.667 0.774 0.4 0.20
0.40.6 0.81
TPR
TPR
0.042 0.055
0.022 0.015
0.048 0.065
0 0.02 0.04 0.06 0.08
FPR
FPR
0.95 0.94 0.97 0.98
0.95 0.93 0.920.9
0.940.96 0.981
TNR
TNR
0.56 0.57 0.55
0.82 0.86 0.66
0.20 0.40.6 0.81
ROC
ROC
7 Conclusion with the Future work
Best motivation of write this paper is due to huge research on Software Defect Prediction(SDP) using intelligent classification, most of the researchers used common dataset like NASA Dataset. Weka also supposes to reduce the complexities of execution for various types of data mining methods. Took reports from weka and compare the performance among classifier algorithms, data set with all defects all most all algorithms perform equally except Naïve Bayes so we further continued our study of classification algorithms how these perform on separate defects. This research feel as baseline and in future research going on SDP with merged classification algorithms, and we would like to move my research on another way is get same performance by using less number of attributes in NASA dataset.
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Authors Biography
K. Eswara Rao received the Bachelor's degree in Engineering (CSE) from GITAM Engineering College, Visakhapatnam, AP, India, in 2005, and the Master’s degree in Engineering (CSE-NN) from JNT University, Kakinada, AP, India, in 2009. He is currently working as a Sr. Assistant Professor in Aditya Institute of Technology and Management (AITAM), Tekkali, Srikakulam. His research interests include Data Mining, Data Analytics, and Operating System. He has published numerous conference proceedings as well as papers in international journals.
Dr. G. Appa Rao completed his Ph.D. and now he is currently working as a Professor in Department of CSE, GITAM Institute of Technology, GITAM (Deemed to be University), Visakhapatnam. His research interest includes software engineering, Wireless Sensor Network. He wrote two chapters named as Microelectronics, Electromagnetics and Telecommunications and Communications in Computer and Information Science. He published numerous conference proceedings as well as papers in national and international journals including Scopus indexed.
Dr. S.Anuradha is an Assistant Professor in the Department of Computer Science and Engineering, GITAM Institute of Technology, GITAM (Deemed to be University), Visakhapatnam. She has received her B.E from Nagpur University, Master Degree (M.Tech) from Andhra University, Visakhapatnam and received her Ph.D. award in Image Processing at JNT University, Kakinada. Her research Interest in Image Processing, Data Mining. She has published more than 10 research papers in reputed national international journals including Scopus indexed.
