Automated Software Testing Cases Generation Framework to Ensure the Efficiency of the Gesture Recognition Systems by Sheikh Monirul Hasan. This is a research work for creating a standard or benchmark for testing gesture recognition system software. sheikh monirul Hasan is the first author of the research paper, the complete work summary of the research we tried to discuss in the presentation slide. so there have many kinds of software engineering think and how we get a quality product specially for gesture recognition system.

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22nd International Conference on Computer and Information Technology (ICCIT)
Automated Software Testing Cases Generation
Framework to Ensure the Efficiency of the
Gesture Recognition Systems
Presented by:
Sheik Monirul Hasan
Authors:
1. Sheik Monirul Hasan
2. Md.Saiful Islam
3. Md.Ashaduzzaman
4. Dr. Muhammad Aminur
Rahaman
Dept. of Computer Science and Engineering
Green University of Bangladesh

2. Contents
1
• Introduction
• Objectives
• Problem Domain
• Related Research
• Motivation
• Proposed Methodology
• Experimental Result Analysis
• Research Contribution
• Conclusion

3. Introduction
What is software testing?
•Software testing is an activity to detect the bug and released a
quality product that is met with the specification of customers.
Software testing standards
•ISO/IEC 29119-2:2013 – Testing Processes
•ISO/IEC 29119-4:2015 – Testing Techniques
2

4. Objective
• To generate a testing case for gesture recognition systems automatically
• To create an automated testing framework
• To improve the quality of software by detecting the defects of the
existing systems
• To minimize the training and testing time
• To minimize training and testing cost
• To build a standard system for testing any gesture recognition system
3

5. Problem Domain
• The existing system prepares input images for training and testing
manually which
Consumes more time
Increase the cost of the system testing and training
• The gesture recognition system has no a recognized testing
standard
• Limited image samples are used in training
• Very few testing cases are used in testing 4

6. Related Research
5
Title: Real-Time Computer Vision-Based Bengali Sign Language Recognition
Reference. [1] Muhammad Aminur Rahaman, Mahmood Jasim, Md. Haider Ali and Md. Hasanuzzaman, “Real-Time
Computer Vision-Based Bengali Sign Language Recognition” Department of Computer Science & Engineering
University of Dhaka, 17th Int'l Conf. on Computer and Information Technology, 22-23 December 2014, Daffodil
International University, Dhaka, Bangladesh

7. Related Research
6
Result :
• Average Accuracy is 96.17%
• Computational Cost is 93.55 Milliseconds/frame
Limitations:
• The system cannot properly segments hand area if some objects rather
than hand has skin like colors.
• The system cannot properly distinguish some signs such as “র” with “ল”
and “ফ”with “ঝ” because of their similarity among their binary images
• The performer faces difficulties to perform few alphabets such as“o, খ, জ,
ঠ, র” due to camera position.

8. Related Research
7
Title: Real-Time Bengali and Chinese Numeral Signs Recognition Using Contour Matching
Reference: [2] M. A. Rahaman, M. Jasim, T. Zhang, M. H. Ali, and M. Hasanuzzaman, “Real-time
bengali and chinese numeral signs recognition using contour matching,”in2015 IEEE International
Conference on Robotics and Biomimetics (ROBIO).IEEE, 2015, pp. 1215–1220.
Methodology:

9. Related Research
8
Result:
• Total accuracy is 95.9%
Limitations:
• In future recognizing the hand sign from the video data.

10. Related Research
9
Title: A Real-Time Hand-Signs Segmentation And Classification System Using Fuzzy Rule
Based RGB Model And Grid-Pattern Analysis
Reference: [3] M. Rahaman, M. Jasim, M. Ali, T. Zhang, and M. Hasanuzzaman, “A real-timehand-signs
segmentation and classification system using fuzzy rule based rgbmodel and grid-pattern
analysis,”Frontiers of Computer Science, vol. 12, 11 2018.

11. Related Research
10
Result:
• The system achieves the mean accuracy of 98.40% for E1,
• 97.1% for E2, 96.60% for E3, 96.20% for E4,
• 95.80% for E5, 95.30% for E6 and 96.57%
Limitation :
• The system may fail to segment the hand-signs,
• If any skin-color objects with similar motion of hand-signs are
presented in the ROI.

12. Related Research
11
Title: Bangla Language Modeling Algorithm For Automatic Recognition of Hand-Sign-
Spelled Bangla Sign Language
Reference: [4] M. Rahaman, M. Jasim, M. Ali, and M. Hasanuzzaman, “Bangla language mod-eling algorithm for
automatic recognition of hand-sign-spelled bangla sign lan-guage,”Front. Comput. Sci., p. 0, 2018

13. Related Research
12
Result :
• The system achieves mean accuracy of 93.50% for words, 95.50%
for composite
• Numerals and 90.50% for sentences recognition in BdSL
Limitation :
• The system sometimes fails to distinguish two similar binary signs
such as ‘র’ and ‘ল’
• Which the color images of them are distinguishable

14. Related Research
13
Title: Hand Sign to Bangla Speech: A Deep Learning in Vision based system for
Recognizing Hand Sign Digits and Generating Bangla Speech.
Reference: [5] S. Ahmed, M. Islam, J. Hassan, M. U. Ahmed, B. J. Ferdosi, S. Saha, M. Sho-ponet al.,
“Hand sign to bangla speech: A deep learning in vision based systemfor recognizing hand sign digits
and generating bangla speech,”arXiv preprintarXiv:1901.05613, 2019.
Methodology:

15. Related Research
14
Result:
• Notable fact is without image augmentation the validation loss result
was incredibly reduced from 0.37 to 0.31.
• Where the accuracy was increased from 89% to 92%.
Limitation:
• Sign language to voice output can eliminate a baiter
• Communication between speech impaired people
.

16. Related Research
15
Title: Elaborating Software Test Processes and Strategies
Methodology:
• This study is to build a reference model for practical application
• Test Strategy defined in the ISO/IEC 29119.
• Focuses on the system Compose testing strategy;
• Human resources, test tools, test case selection, testing methods and the
role of the management The test process to name a few of the major
components.
Limitation:
• There is needed other software testing standard.
Reference: [6] Jussi Kasurinen,“Elaborating Software Test Processes and Strategies”, 2010 Third
International Conference on Software Testing, Verification and Validation , p.p. 10 November 2010

17. Motivation
16
• To create an automated testing framework
• To minimize the time of training and testing sample generation
• To minimize cost of training and testing
• To create large number of test cases automatically from limited
number of sample images
• To build a standard for testing any gesture recognition system

18. Common Testing Parameters for Proposed System
17
TABLE I: Consideration Common Parameters of Testing in Each Existing System
to Generate Test Cases.
Systems
Test Cases
Rotation Contrast Scale Backgrounds Noise
System-1 (Rahaman et. al. [1]) × c × × c
System-2 (Rahaman et. al. [2]) c c c × ×
System-3 (Rahaman et. al. [3]) c c c c c
System-4(Rahaman et. al. [4]) c c c c c
System-5 ( Shahjalal Ahmedet. al. [5]) c c c × ×

19. Why we Choose Those Parameters
18
• We are analysis some system
• Every system use the nearest same feature for processing images
and train up and testing their system
• We take some common features
• That features also uses every system
• Our proposed model has five parameters
• To test the existing system

20. Proposed Methodology
19
Fig.6. The architecture of the proposed automated testing framework for gesture recognition systems.
Input = n
Rotation
Contrast
Scale
Background
Noice
Testing System
T= R+C+S+B+N
Output
Test case
Generation
Testing
Phase
Number of
Defect
R
C
S
B
N

21. Different Test Cases
20
TABLE II: Comparative Analysis of Generated Testing Cases Between Our Proposed
System and Other Existing Systems
Method
Total No.of
sample(p)
No of test sample created by our Proposed
Framework
Existing Methode
sample image by per letter Total test
Images (p*q)
Per letter
sample
image(j)
Sample
Data (k)
Total Test
Images(j*k)Rotat
ion
Cont
rast
Scal
e
Backgr
ound
Noise
Total
(q)
System-1 (Rahaman
et. al. [1])
36 100 100 100 100 100 500 18000 100 36 3600
System-2 (Rahaman
et. al. [2])
10 100 100 100 100 100 500 5000 100 10 1000
System-3 (Rahaman
et. al. [3])
46 100 100 100 100 100 500 23000 100 46 4600
System-4(Rahaman
et. al. [4])
52 100 100 100 100 100 500 26000 100 52 5200
System-5 ( Shahjalal
Ahmedet. al. [5])
10 100 100 100 100 100 500 5000 100 10 1000

22. Algorithm
21
Data: A set of Images I, an Existing Testing Model µ
Result: Accuracy Tac for the set of Images in I tested on
Model µ
Procedure:
R; B; S; C; N I
for each i image in I do
R R U GetRotatedSamples for i
B B U GetDifferentBackgroundSamples for i
S S U GetScalingSamples for i
C C U GetContrastSamples for i
N N U GetNoisedSamples for i
end
Algorithm 6 : Algorithm to Implement the Proposed Testing Framework.

23. Algorithm Cont…
22
for each sample in R do
Calculate accuracy Rac using Model µ
end
for each sample in B do
Calculate accuracy Bac using Model µ
end
for each sample in S do
Calculate accuracy Sac using Model µ
end
for each sample in C do
Calculate accuracy Cac using Model µ
end
for each sample in N do
Calculate accuracy Nac using Model µ
end
Tac = (Rac + Bac + Sac + Cac + Nac)/5
Return: Return Tac

24. Test Case-1: Different Rotation
23
Example : Using rotation we can get different type of images .
23
(a) Rotation 0° (b) Rotation -10° (c) Rotation -20° (d) Rotation -30° (e) Rotation -45°
(f) Rotation 0° (g) Rotation +10° (h) Rotation +20° (i) Rotation +30° (j) Rotation +45°
Fig.4. Example of Test Images Generated by the Proposed System for Different Rotation.

25. Test Case-2: Different Contrast
2424
Example : Using contrast we can get different type of images .
24
(a) Contrast 0 (b) Contrast -20 (c) Contrast -40 (d) Contrast -60 (e) Contrast -80 (f) Contrast -100
(g) Contrast 0 (h) Contrast +20 (i) Contrast +40 (j) Contrast +60 (k) Contrast +80 (l) Contrast +100
Fig.6. Example of Testing Images Generated by the Proposed System with Different Contrasting Factors.

26. Test Case-3: Different Scale
25
Example : Using Size we can get different type of images
25
(a) Scaling 0 (b) Scaling x-5 (c) Scaling x-25 (d) Scaling xy -5 (e) Scaling xy-25 (f) Scaling y-5 (g) Scaling y-25
(h) Scaling 0 (i) Scaling x +5 (j) Scaling x +25 (k) Scaling xy +5 (l) Scaling xy +25 (m) Scaling y +5 (n) Scaling y+25
Fig.5. Example Testing Images Generated by the Proposed System with Different Scaling Factors.

27. Test Case-4: Different Background
26
Example : Using Background we can get different type of images .
26
(a) Background 1 (b) Background 2
(f) Background 6 (g) Background 7 (h) Background 8 (i) Background 9 (j) Background 10
(e) Background 5(d) Background 4(c) Background 3
Fig.7. Example of Testing Images Generated by the Proposed System with Different Backgrounds.

28. Test Case-5: Different Noise
27
Example : We can get a image to many images
27
(a) Noise-1 (b) Noise-2 (e) Noise-5(d) Noise-4
(f) Noise-6 (g) Noise-7 (h) Noise-8 (i) Noise-9 (j) Noise-10
(c) Noise-3
Fig.8. Example Testing Images Generated by the Proposed System with Different Noise Filters.

29. Experimental ResultAnalysis
28
TABLE III: Comparative Analysis of Experimental Results for Each System Using Test
Cases Generated by Our Proposed Testing Framework And Existing Methods
Method
Recognition accuracy using proposed test cases (%) Existing Method
Rotation Contrast Scale Backgrounds Noise mean
mean accuracy
(%)
System-1 (Rahaman et. al. [1]) 60.45 90.58 65.05 80.56 90.3 77.388 96.46
System-2 (Rahaman et. al. [2]) 96.45 90.66 94..85 30.24 50.8 67.038 95.85
System-3 (Rahaman et. al. [3]) 92.5 94.9 95.23 91.73 90.89 93.05 95.67
System-4(Rahaman et. al. [4]) 92.1 93.34 96.13 92.56 92.6 93.346 95.83
System-5 ( Shahjalal Ahmed et. al.
[5])
80.9 85.3 90.45 75.6 65.8
79.61 92.00

30. Research Contribution
29
• We proposed a automated software testing cases generation framework
for gesture recognition system.
• Generating a different kind of testing cases
• Creating lots of training and testing images
• Identified total bug of the existing system
• Showing the total accuracy
• Help to reduce the processing time and computational cost
• Minimize cost of training and testing

31. Conclusion
30
• We have proposed and implemented an automated software testing
framework, especially for the gesture recognition system.
• Through this framework, we can easily test any gesture recognition
system.
• The testing tool works with five parameters such as rotation Algorithm,
contrast Algorithm, scaling Algorithm, background Algorithm and noise
Algorithm.

32. Limitations of The Work
31
• The framework is only used for gesture recognition system testing
• There have many software testing standards but the proposed
framework has used only two common standards
 ISO/IEC 29119-2:2013 - test processes
 ISO/IEC 29119-4:2015 test techniques
• Some times the proposed framework evaluates the performances of
existing gesture recognition tools wrongly

33. Future Works
32
• We can increase the testing cases
• There will be adding other kinds of the testing process like (Unit and
Integration testing)
• Opportunity to tests others kind of image processing system testing
• AI application system Testing
• Others type of software testing standard will also use.

34. Application
33
This system can be use
• To generate automatic test cases to test any gesture recognition system
• To standardized or a benchmark for testing gesture recognition tool
• To identify defect any gesture recognition system

35. References
34
[1] M. A. Rahaman, M. Jasim, M. H. Ali, and M. Hasanuzzaman, “Realtime computer vision-based bengali sign language recognition,” in 2014 17th
International Conference on Computer and Information Technology (ICCIT). IEEE, 2014, pp. 192–197.
[2] M. A. Rahaman, M. Jasim, T. Zhang, M. H. Ali, and M. Hasanuzzaman, “Real-time bengali and chinese numeral signs recognition using contour
matching,” in 2015 IEEE International Conference on Robotics and Biomimetics (ROBIO). IEEE, 2015, pp. 1215–1220.
[3] M. Rahaman, M. Jasim, M. Ali, T. Zhang, and M. Hasanuzzaman, “A real-time hand-signs segmentation and classification system using fuzzy
rule based rgb model and grid-pattern analysis,” Frontiers of Computer Science, vol. 12, 11 2018.
[4] M. Rahaman, M. Jasim, M. Ali, and M. Hasanuzzaman, “Bangla language modeling algorithm for automatic recognition of hand-signspelled
bangla sign language,” Front. Comput. Sci., p. 0, 2018.
[5] S. Ahmed, M. Islam, J. Hassan, M. U. Ahmed, B. J. Ferdosi, S. Saha, M. Shopon et al., “Hand sign to bangla speech: A deep learning in vision
based system for recognizing hand sign digits and generating bangla speech,” arXiv preprint arXiv:1901.05613, 2019.
[6] Jussi Kasurinen,“Elaborating Software Test Processes and Strategies”, 2010 Third International Conference on Software Testing, Verification
and Validation , p.p. 10 November 2010
[7] J. Gao, H.-S. Tsao, and Y. Wu, Testing and quality assurance for component-based software. Artech House, 2003.
[8] G. Davis, “Managing the test process [software testing],” in Proceedings International Conference on Software Methods and Tools. SMT 2000,
Nov 2000, pp.119–126.
[9] A. Akoum and N. Al Mawla, “Hand gesture recognition approach for asl language using hand extraction algorithm,” Journal of Software
Engineering and Applications, vol. 8, no. 08, p. 419, 2015.

36. Question & Answer
35
Any Question?Any Question?

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