Classification and Prediction of High and Low Maintainable Class of

Object Oriented Systems at Design Level using Machine Learning

Techniques

Anshita Malviya

M.Tech. Scholar, Madan Mohan Malaviya University of Technology, Gorakhpur (UP) India

Keywords: Object Oriented System, Machine Learning Technique, Object Oriented System, Maintenance.

Abstract: In software engineering, maintenance is the one of the most crucial, costly and difficult activity. Numerous

research works are still going on in this area to reduce and measure the maintenance cost. The maintenance

consumes up to 80% of the total software development cost. There is a trend of developing software using

object oriented techniques due to obvious reasons. In this paper, we proposed a classification model to

identify high and low maintainable class at design level of Object Oriented Software development process.

This model is implemented in python using Machine Learning Techniques. Experiment is simulated on

Jupyter Notebook.

INTRODUCTION

Software quality is paramount importance for users as

well as for development community. It is difficult to

measure quality in software but its absence makes a great

difference. Designs phase has a crucial impact on

software maintainability. Class is a fundamental unit of

object-oriented systems. Therefore it is a good idea to

design an object-oriented systems keeping in view of

maintainability of a class as a prime concern.

Machine learning, a subset of artificial intelligence, is

so versatile today that we use it several times in a day

without having knowledge of it. We cannot imagine this

world without machine learning as we already got so

many things from it and in future will also get. Learning

is a native behavior of living beings. Living beings gets

new knowledge from the surrounding and modify it by

experiences like happiness and hurdles which comes on

their way. Simulating the learning ability of living beings

into machines is what we all know as machine learning.

Figure 1 depicts the phase wise cost of software

development process. There are different phases in

software development process which are requirement,

design, coding and testing. Requirement, design, coding,

testing and maintenance phases respectively take 3%,

8%, 7%, 15% and 67% of total development cost.

As we know design phase gives a crucial impact on

software maintenance. Therefore, it is good idea to

design an object oriented system keeping in mind the

view of the maintainability as the prime concern.

Figure 1: Phase wise cost of software process

The object oriented design phase consists of the

hierarchy of the entire classes that are used to build a

software system. Therefore in design phase, we build a

model which tells the software designer that whether a

class is high or low maintainable class. The designer can

modify the high maintainable classes into low

maintainable classes. This way the overall cost of the

software system will be reduced.

Rest of the paper is organized as follows. Section II

introduced the literature survey related to the Software

170

Malviya, A.

Classiﬁcation and Prediction of High and Low Maintainable Class of Object Oriented Systems at Design Level using Machine Learning Techniques.

DOI: 10.5220/0010564700003161

In Proceedings of the 3rd International Conference on Advanced Computing and Software Engineering (ICACSE 2021), pages 170-176

ISBN: 978-989-758-544-9

maintenance. Proposed methodology is presented in

Section III. Proposed work is explained in Section IV.

Section V presented result analysis. Conclusion is given

in section VI. Future work is proposed in section VII.

2 RELATED WORK

In this section, we presented the main works of various

authors related to the prediction of maintenance effort

using different techniques.

Li W. and Henry S. concentrated on various metrics

of object oriented software and the validation of these

metrics with maintenance effort in two business systems.

The author proposed the prediction model involving 10

object oriented metrics. Abdulrahman A. B. B. et al.

proposed a model to predict software maintainability

using Evolutionary Neural Network (NN).

Kaur A., Kaur K., and Malhotra R. Et al. constructed

various models using Artificial Neural Networks, Fuzzy

Inference Systems and Adaptive Neuro-Fuzzy Inference

Systems for prediction and comparison of maintenance

effort.

Marounek Petr measured software maintenance

effort estimation and historical experience using PERT

formula.Ebert C. And Soubra H. studied estimation

technologies which can be applied to maintenance

projects based on the COSMIC (Common Software

Measurement International Consortium. ISO/IEC

19761:2011) method.

Ahn Y. et al. discussed software maintenance size and

the software maintenance effort estimation model

(SMPEEM). The significance of the SMPEEM model is

shown by applying regression analysis on small

maintenance projects.

Lucia A.D. et al presented an effort estimation

models for corrective maintenance projects. They

validated the proposed model to a new corrective

maintenance project within the same organization. They

also made multiple regression model to improve the cost

prediction model which is currently used in large

software organization.

Sheela G. A. S. Et al. proposed maintenance effort

prediction models using numerous statistical techniques

with the help of object-oriented cognitive complexity

metrics.

Malviya A.presented a comparison of some

supervised learning techniques on two data sets. Further,

Author also described machine learning approaches and

classification techniques of Machine Learning.

3 PROPOSED METHODOLOGY

Methodology used to make classification model consists

of the following steps given below and also depicted in

Figure 2.

1. Take maintenance Data set in terms of Object

Oriented Metrics

2. Pre-processing of Data set if required

3. Build models with machine learning techniques like

K-means clustering, Hierarchical clustering etc.

4. Compare Accuracy of different models

5. Predict a given class in terms of High and Low

maintainable classes

Figure 2: Methodology to identify high and low

maintainable class

4 PROPOSED WORK

In this section, we developed the classification model

using methodology as mentioned in the previous section

with the help of machine learning techniques. Further,

we explored how to develop classification model step by

step in Machine Learning.

Classiﬁcation and Prediction of High and Low Maintainable Class of Object Oriented Systems at Design Level using Machine Learning

Techniques

171

4.1 Collect Data

The first step involved in developing the classification

model is collecting data. The goodness of the predictive

model is based on the quality and quantity of the data

collected which turn out to be one of the most important

step in developing a machine learning model.

The maintenance effort data have been taken from

the research paper [3]. The number of lines changed in a

class indicates the maintenance effort given by the

developer. Addition or deletion of a line in a

program/software is used as a measure of line change.

The dataset consist of 8 object oriented metrics, 2

size oriented metrics and 1 change metric which are DIT

(depth in the inheritance tree), NOC (number of

children), MPC (message passing coupling), RFC

(response for class), LCOM (lack of cohesion of

methods), DAC (data abstraction coupling), WMC

(Weighted method complexity), NOM (number of

methods), SIZE1 (number of semicolons per class),

SIZE2 (number of methods plus number of attributes)

and Change (number of lines changed per class in the

maintenance history).

4.2 Prepare and Analyse the Input

Data

After the training data is collected, we move on to The

next step of machine learning is: Data preparation which

is done after collecting the data. Both the datasets used in

this paper are gathered in the form of hardcopy and then

they are stored in the excel form and finally converted to

CSV file format.

Figure 3 represents the Box and Whisker Plot of

UIMS dataset and Figure 4 represents the Box and

Whisker plot of QUES dataset.

Figure 3: Box and Whisker plot of all attributes of UIMS

dataset

Figure 4: Box and Whisker plot of all attributes of QUES

dataset

Figure 5 and 6 represents the Correlation Matrix

Plot of UIMS dataset and QUES dataset

respectively. Correlation matrix is used to measure

the correlation between each pair of metrics.

Figure 5: Correlation matrix of attributes of UIMS dataset

Figure 6: Correlation matrix of attributes of QUES dataset

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4.3 Choose Model

Keeping the objective of the paper in mind the K-

means clustering technique is selected. We have

taken two dataset uims dataset and ques dataset. The

metrics taken in both dataset are DIT, NOC, MPC,

RFC, LCOM, DAC, WMC, NOM, SIZE2. The

reason behind taking these metrics is that they are

available at the design level of any Object Oriented

Software thus fulfilling our motive of the paper.

4.4 Train the Model

Before training the models we founded the value of

k(number of clusters). For finding the optimal value

of k, two techniques are used namely Elbow method,

Average silhouette method. Table 1 presents sum of

squared distance of clusters of UIMS dataset

whereas Table 2 presents number of clusters versus

Silhouette score of UIMS dataset.

Table 1: Sum of squared distances of clusters of UIMS

dataset

Number of

clusters

Sum of squared

distance(SSD)

1 38600.56

2 10929.28

3 7550.28

4 5069.28

5 3469.48

6 2921.76

7 2376.75

8 1937.80

9 1662.89

10 1316.72

11 1021.57

12 826.53

13 660.73

14 570.58

Figure 7 represents sum of squared distances and

value of k for UIMS dataset. This graph is used to

find out the optimal value of k using Elbow method.

Figure 8 represents silhouette average and value of k

for UIMS dataset. This graph is used to find out the

optimal value of k using Silhouette method.

Figure 7: Graph for finding the optimal of k for UIMS

dataset

Table 2: Number of clusters versus Silhouette score of

UIMS dataset

Number of

clusters

Silhouette

Score

2 0.71

3 0.69

4 0.43

5 0.42

6 0.33

7 0.30

8 0.31

9 0.30

10 0.35

11 0.35

12 0.37

13 0.38

Figure 8: Graph for finding the optimal of k for UIMS

dataset using Silhouette method

Classiﬁcation and Prediction of High and Low Maintainable Class of Object Oriented Systems at Design Level using Machine Learning

Techniques

173

Table 3: Sum of squared distances of clusters of QUES

dataset

Number of

clusters

Sum of

squared

distance(SSD)

1 130444.70

2 43930.00

3 29847.63

4 24536.45

5 20151.6

6 17097.58

7 14553.65

8 11349.45

9 9216.66

10 7461.76

11 5840.61

12 5193.16

13 4434.66

14 4003.12

Figure 9 represents sum of squared distances and

value of k for QUES dataset. This graph is used to

find out the optimal value of k using Elbow method.

Figure 10 represents silhouette average and value of

k for QUES dataset. This graph is used to find out

the optimal value of k using Silhouette method.

Figure 9: Graph for finding the optimal of k for QUES

dataset

Table 4: Number of clusters versus Silhouette score of

QUES dataset

Number of

clusters

Silhouette

score

2 0.65

3 0.54

4 0.53

5 0.32

6 0.41

7 0.46

8 0.42

9 0.43

10 0.45

11 0.44

12 0.42

13 0.42

Figure 10: Graph for finding the optimal of k for QUES

dataset using Silhouette Method

From the above experiment we get to know that

the optimal number of clusters for both dataset using

elbow method is 3 and using silhouette method is 2.

Next we applied the K-means

clustering technique

with number of clusters 2 and 3 for both the datasets.

We divided both datasets into training set and test

set. Training set consist of the 80% of the data and

remaining 20% for the test set. For this we used

X_train, X_test, y_train,

y_test=train_test_split(X, y, test_size=.20).

We used X_train for training the k-means clustering

technique on 2 and 3 clusters for both datasets. The

X_test used to predict the performance of the model.

km=KMeans(n_clusters=2/3,init='k-

means++', n_init=10, max_iter=300,

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tol=1e-4,precompute_distances='auto',

n_jobs=None, random_state=11, verbose=0,

copy_x=True, algorithm='auto')

km.fit(X_train).

4.5 Evaluate the Model

We evaluated the models using confusion matrix,

classification report and accuracy metrics.

y_pred=km.predict(X_test). Table 5 presents

confusion matrix for 3 clusters of UIMS and QUES

dataset whereas Table 6 represents confusion matrix

for 2 clusters of the both datasets.

Table 5: Confusion matrix for 3 Clusters of UIMS and

QUES

Dataset

Table 6: Confusion matrix for 2 Clusters of UIMS and

QUES dataset

Uims dataset Ques dataset

Low High Low High

Low 5 1 0 2

High 0 2 0 13

Table 7 and Table 8 represent classification

reports for 3 clusters and 2 clusters respectively for

the both datasets.

Table 7: Classification Report for 3 Clusters of UIMS and

QUES dataset

Table 8: Classification Report for 2 Clusters of UIMS and

QUES dataset

Table 9 and Table 10 presents accuracy score for 3

and 2 clusters respectively for the both datasets.

Table 9: Accuracy Score for 3 Clusters of UIMS and

QUES dataset

Uims

dataset

Ques

dataset

Accuracy 0.875 0.73

Table 10: Accuracy Score for 2 Clusters of UIMS and

QUES dataset

Uims

dataset

Ques

dataset

Accuracy 0.875 0.87

5 RESULT ANALYSIS

In this section, we explained the results obtained in

previous section. Following are some observations:

 For UIMS dataset for 3 clusters Table 7 reveals

that precision for predicting the high

maintainable classes is maximum and low

maintenance is minimum. Medium and high

maintainable classes have secured recall value

1. The maximum f1-score is for high maintained

class. Table 7 also shows the different

performance values of ques dataset for 3

clusters. Precision values are 1.00, 0.80 and

0.00 for high, medium and low maintainable

classes respectively. But recall and f1-score

values are maximum for medium maintainable

classes.

 Table 8 depicts the classification reports for 2

clusters of uims and ques dataset. For uims

dataset, precision and f1-score are maximum for

low maintainable classes and recall value is

minimum for low maintainable class. For ques

dataset precision, recall, f1-score all 3 values are

0.87, 1.0, 0.93 that are maximum. It is seen

from Table 9 and 10 that accuracy is maximum

Classiﬁcation and Prediction of High and Low Maintainable Class of Object Oriented Systems at Design Level using Machine Learning

Techniques

175

of uims dataset for 3 clusters and for 2 clusters

also.

6 CONCLUSIONS

We developed classification model to identify high

and low maintainable classes at the early stage of

development of Object Oriented Software System.

This model acts as a warning to software designer

about the quality of design of the proposed system.

Further this model is also used to reduce the cost of

maintenance of the proposed system.

FUTURE WORK

1. Principal Component Analysis can be used to

minimize attributes for both clustering model.

2. Classification techniques like decision tree, naïve

base and random forest can be used.

3. Other clustering techniques can be used.

4. Other big data sets are required and needed to

make specific comments in this research

direction.

5. Maintenance effort model can also be made.

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Scientists”, O’REILLY

Manohar Swamynathan, “Mastering Machine Learning

with Python in Six Steps-A Practical Implementation

Guide to Predictive Data Analysts using Python”,

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