Using Fuzzy Clustering and Software Metrics to Predict Faults in large Industrial Software Systems

IOSR Journal of Computer Engineering (IOSR-JCE)
e-ISSN: 2278-0661, p- ISSN: 2278-8727Volume 13, Issue 6 (Jul. - Aug. 2013), PP 32-36
www.iosrjournals.org
www.iosrjournals.org 32 | Page
Using Fuzzy Clustering and Software Metrics to Predict Faults in
large Industrial Software Systems
Nurudeen Sherif1
, Nurudeen Mohammed2
1
(Faculty Of Informatics/ Universiti Sultan Zainal Abidin, Malaysia)
2
(School Of Information Science and Engineering / Central South University, China)
Abstract : Faults are a key problem in software systems. Awareness of possible flaws from the initialization of
a project could save money, time and work. Estimating the possible deficiency of software could help in
executing software development activities. This paper proposes a model to predict the possibility of faults on a
software system before testing. The model predicts possible faults during software development using Fuzzy
Clustering and Software Metrics. This research is aimed at predicting faults in large software systems by
creating clusters and then finding out the distance of each point in the data set with the clusters created to
determine their degree of membership within each cluster
Keywords: Software, fault prediction, software metrics, fuzzy clustering
I. INTRODUCTION
Reliance on software in our daily lives has increased so much in the last decade that in our day living
without devices controlled by software is almost impossible. The Industrial domains such as medical
applications, power plants, air traffic control and railway signaling have all integrated software as a fundamental
part of their operation. Software engineers have to deal with a large number of quality requirements such as
reliability, safety, availability, performance, maintainability and security which makes the development of these
large software applications very challenging. The industrial reliance on software gives rise to the likelihood of
gross crises in the case of a failure and the effect of these catastrophes ranges from economic damage to loss of
lives. Therefore, there is an increasing necessity to ensure the steadfastness of software systems. Moreover, it is
well known that the earlier a problem can be identified, the better and more cost effectively this problem can be
fixed. Therefore, it is necessary to predict faults during the software development.
There are numerous techniques and metrics for investigating fault prone modules which may aid
software developers in performing testing activities during development. It is almost impossible to produce
software that is free of faults due to the rising complexity and the constraints under which the software is
developed. Such faults may lead to an increment in development & maintenance cost and time, due to software
failures and decrease customer’s satisfaction [1].
Data Clustering is a basic technique in many modeling algorithms. The objective of clustering is to
construct new collections of data from large data set. One of the most acceptable contributions to the field of
data clustering is Fuzzy C-Means clustering. It has more benefits compared to other methods of data clustering,
specifically the ability to split data for different size clusters with fuzzy logic. The Fuzzy C-Means can be seen
as the modified version of the k-means algorithm. Which is a method of clustering that allows one piece of data
to belong to two or more clusters. The degree of being in a certain cluster is related to the inverse of the distance
to the cluster [2]. Fuzzy C-Means iteratively moves the cluster centers to the "right" location within a data set.
This research is aimed at predicting faults in large industrial software systems by creating clusters and then
finding out the distance of each point in the data set with the clusters created to determine their degree of
membership within each cluster. The Factors like Mean Absolute Error, Accuracy and Root Mean Square Error
help us in predicting the software system as faulty or fault-free.
The literature, [3]-[17] presents various types of Fault-Proneness Estimation Models. The results are
also compared with [18] in which Hierarchical clustering based approach is used for Finding Fault Prone
Classes in large software systems. The paper is organized as follows: section II exploits some literature on
related works, section III explains the methodology followed in this research and section IV the result of the
study. Finally conclusions of the research are presented in section V.
II. RELATED WORKS
Quite a number of efforts have been made in research for software fault prediction and assessment
using various techniques [3] – [5]. Agresti and Evanco [6] worked on a model to predict defect density based on
the product and process characteristics for Ada program. There are many papers advocating statistical models
and software metrics [7, 8]. Gaffney and Davis [9, 10] of the Software Productivity Consortium developed the

Using Fuzzy Clustering and Software Metrics to Predict Faults in large Industrial Software Systems
phase-based model. It uses fault statistics obtained during the technical review of requirements, design, and the
coding to predict the reliability during test and operation.
One of the earliest and well known efforts to predict software reliability in the earlier phase of the life
cycle was the work initiated by the Air Force’s Rome Laboratory [11]. For their model, they developed
prediction of fault density which they could then transform into other reliability measures such as failure rates.
To do this the researchers selected a number of factors that they felt could be related to fault density at
the earlier phases. Most of them are based on size and complexity metrics. In order to achieve high software
reliability the number of faults in delivered code should be reduced. The faults are introduced in software in
each phase of software life cycle and these faults pass through subsequent phases of software life cycle unless
they are detected through testing or review process. Finally, undetected and uncorrected faults are delivered with
software. In order to achieve the target software reliability efficiently and effectively, faults should be identified
at early stages of software development process. During early phase of software development testing/field
failure data is not available. Therefore, the prediction is carried out using various factors relevant to reliability.
A study was conducted by Zhang and Pham [12] to find the factors affecting software reliability. The
study found 32 potential factors involved in various stages of the software life cycle. In another recent study
conducted by Li and Smidt [13], reliability relevant software engineering measures have been identified. They
have developed a set of ranking criteria and their levels for various reliability relevant software metrics, present
in the first four phases of software life cycle. Recently, Kumar and Misra [14] made an effort for early software
reliability prediction considering the six top ranked measures given by [13] and software operational profile.
Sometimes, it may happen that some of these top ranked measures are not available, making the prediction
result unrealistic. Also they have considered only product metrics and ignored process metrics that have a great
impact on software reliability [15].
Software metrics can be classified in three categories: product metrics, process metrics, and resources
metrics [16]. Product metrics describe characteristics of the product such as size, complexity, design features,
performance and quality level etc. Process metrics can be used to improve software development process and
maintenance. Resources metrics describe the project characteristics and execution. Approximately thirty
software metrics exist, which can be associated with different phases of software development life cycle.
Among these metrics some are significant predictor to reliability [13]. From the above literature we have
observed that
1. Predicting faults early is very important for the entire software development process and reliability.
2. The reliability of software is a function of the number of the remaining faults.
3. Software metrics plays a vital role in early fault prediction in the absence of failure data.
Review of literature indicates that traditional models have not considered the both software metrics and
development process maturity, for the early fault prediction. Therefore this paper proposes a model for early
software fault prediction considering software metrics and process maturity together.
III. METHODOLOGY
There are several ways of identifying fault prone modules in a software application. First of all, find the
structural code and design attributes of software systems. Thereafter, select the suitable metric values as
representation of statement. Next step is to analyze, refine metrics and normalize the metric values. We used
JEdit open source software in this study. JEdit is a programmer's text editor developed using Java language.
JEdit combines the functionality of Window, UNIX, and Mac OS text editors. It was released as free software
and the source code is available on [20]. JEdit includes 274 classes. The number of developers involved in this
project was 144. The project was started in 1999. The number of bugs was computed using SVC repositories.
The release point for the project was identified in 2009. The log data from that point to 2012 was collected. The
header files in C++ were excluded in data collection. The word bug or fixed was counted. Details on bug
collection process can be found in [19]. The following are the metrics used in the classification process:
1. Coupling between Objects
2. Lack of Cohesion
3. Number of Children
4. Depth of inheritance
5. Weighted Methods per Class
6. Response for a class
7. Number of Public Methods
8. Lines of Code

Using Fuzzy C Means Clustering algorithm we can group the software components into faulty and fault-free
systems. Clustering can be a very effective technique to identify natural groupings in data from a large data set,
thereby allowing concise representation of relationships embedded in the data. In our study, clustering allows us
to group software modules into faulty and non-faulty categories hence allowing for easier understandability.
Fig.1 A flowchart depicting Fuzzy C - Means Clustering algorithm.
The Fuzzy C - Means Clustering algorithm attempts to partition a finite collection of n elements
into a collection of c fuzzy clusters with respect to some given criterion. Given a
finite set of data, the algorithm returns a list of c cluster centres
and a partition matrix ,
where each element wij tells the degree to which element xi belongs to cluster cj . Like the k-means
algorithm, the Fuzzy C - Means aims to minimize an objective function. The standard function is:
Any point x has a set of coefficients giving the degree of being in the kth cluster wk(x). With Fuzzy C -
Means, the centroid of a cluster is the mean of all points, weighted by their degree of belonging to the cluster:
The degree of belonging, wk(x), is related inversely to the distance from x to the cluster center as calculated
on the previous pass. It also depends on a parameter m that controls how much weight is given to the closest
center.
To predict the results, we have used confusion matrix as shown in Table I. The confusion matrix has
four categories: True positives (TP) are the modules correctly classified as faulty modules. False positives (FP)
refer to fault-free modules incorrectly labeled as faulty. True negatives (TN) are the fault-free modules correctly
labeled as such. False negatives (FN) refer to faulty modules incorrectly classified as fault-free modules.
Start
Database
Xnp
C = clusters
M = scaling factor
G = shaping matrix
ε = error tolerance
α = membership
tolerance
Time = 0
Initial Partitions
Ũ(0)
Calculate Fuzzy Cluster
Centers
Φ = ||Ũ(1+1)
– Ũ(0)
||G
Calculate new partitions:
Ũ(1+1)
Φ > ε
Apply α - cuts
Stop
t = t+1

Table I
Matrix of Prediction
Prediction
Data
Fault No Fault
Fault TP FP
No Fault FN TN
The following set of evaluation measures are being used to find the results:
1. Mean Absolute Error is a quantity used to measure how close forecasts or predictions are to the
eventual outcomes.
2. Root Mean Square Error is a quadratic scoring rule which measures the average magnitude of the
error. The difference between forecast and corresponding observed values are each squared and then
averaged over the sample. Finally, the square root of the average is taken.
3. Accuracy: It indicates proximity of measurement results to the true value, precision to the repeatability
or reproducibility of the measurement.
The accuracy is the proportion of true results (both true positives and true negatives) in the population. The
Mean Absolute Error and the Root Mean Square Error can be used together to diagnose the variation in the
errors in a set of forecasts. The Root Mean Square Error will always be larger or equal to the Mean Absolute
Error; the greater difference between them, the greater the variance in the individual errors in the sample. If the
Root Mean Square Error = Mean Absolute Error, then all the errors are of the same magnitude. Both the Mean
Absolute Error and Root Mean Square Error can range from 0 to ∞. They are negatively-oriented scores:
Lower values are better.
IV. RESULTS
During prediction the True positives (TP) is calculated as 18, means 18 modules are correctly classified
as faulty modules. False positives (FP) calculated as 28, means 28 fault-free modules incorrectly labeled as
faulty. True negatives (TN) is calculated as 228, means 228 modules are the fault-free modules correctly labeled
as such and False negatives (FN) comes out to be 4, means 4 faulty modules incorrectly classified as fault-free
modules. These values are recorded in confusion matrix as shown in Table II.
Table II
Recorded Matrix of Prediction
Prediction
Data
Fault No Fault
Fault 18 28
No Fault 4 228
The Root Mean Square Error and Mean Absolute Error are thus calculated as 0.3393 and 0.1151 respectively
while the accuracy of prediction is calculated as 88.49%.
V. CONCLUSION
This paper empirically evaluates performance of Fuzzy Clustering technique in predicting fault-prone
classes in large industrial software. Here, the System generated from fault data using Fuzzy Clustering in
MATLAB 7.4 environment is evaluated for the JEdit testing dataset. The proposed Fuzzy C Means Clustering
based prediction technique shows the results are 88.49% percent Accuracy. This study confirms that
construction of Fuzzy C Means Clustering based model is feasible and useful in predicting faulty prone classes.
It is therefore concluded that, in case of large software systems, model is implemented using Fuzzy C Means
Clustering based technique for classification of the software components into faulty/fault-free systems is found
satisfactory. The contributions of the study can be summarized as follows: First large software systems
analyzed. These systems are developed with different development methods than proprietary software. In
previous studies mostly proprietary software were analyzed. Second, we examine Fuzzy clustering method to
predict the faulty classes with better accuracy. The future work can be extended in following directions:
1. Most important attribute can be found for fault prediction and this work can be extended to further
programming languages.
2. More algorithms can be evaluated and then we can find the best algorithm. We plan to replicate our
study to predict model based on hybrid genetic algorithms or soft computing techniques.

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Using Fuzzy Clustering and Software Metrics to Predict Faults in large Industrial Software Systems