Evaluation of Android Audio Player metrics using ...

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analyze android audio player with the different applications which build by different .... chart methods, VIT University, DOI:10.13140/RG.2.2.27422.95041. [18].
International Conference on Innovations in Power and Advanced Computing Technologies [i-PACT2017]

Evaluation of Android Audio Player metrics using Halstead Measure Gowtham Reddy K

T D V Kiran

VIT University, Vellore [email protected] Abstract We exist in the era of technology and science. Now-a-days the study is mainly focusing on various new developments and comparing the already existing knowledge. Software engineering is one of the ultimate branches of Computer Science. One of the evolving areas in the field of Software engineering is the study of metrics. By using software metrics one is able to measure the efficiency of code without its execution. The objective of this study is to analyze the different software metrics for most commonly used applications like audio players of the android (mobile) platform. We will develop and analyze android audio player with the different applications which build by different developers (which has been written in Java language) to analyze the significance of different performance metrics using a tool known as Halstead metrics. Keywords-Component: Android, audio, Java, Halstead, metrics, analysis, software, measure.

I. INTRODUCTION some properties of software and its component. Software researchers are finding it tough to find a little quantitative information from a software component. Software metric is very useful in refining the time spent, quality of software and its cost estimation etc. By using the help of software metric we are able to know the software product in an effective way. We apply software logic of mathematical technique to a software process or product to provide engineering and management information. known as software science researchers have used them 1) to evaluate student programs and query languages. 2) to measure software written for a real-time switching system, 3) to measure functional programs, 4) to incorporate software measurements into a compiler, and more recently 5) to measure open source current commercial tools that count lines of code. According to Halstead, The computer program is an implementation of an algorithm considered to be a group of tokens which can be categorized as either operators or operands. Now in some of the upcoming android audio players, we can see the different features included in it. Some of the features which are included in the list of players.

VIT University, Vellore [email protected]

Equalizer Widgets Themes

Scrabbling

Sleep Timer

TABLE 1. FEATURES OF ANDROID MUSIC PLAYERS Sue Universal Smith Media Jams Kure Tomahawk Yes

Yes

No

Yes

No No No

Yes No

Yes

Yes

Yes

Yes

No

No

Yes

Yes

Yes

Yes

Yes No

Yes No

No

Yes

II. OBJECTIVE

Our main objective of this study is to compare between five android open source audio players (based on various features) using the Halstead metrics. In this study, we consider various factors [5] like a total number of unique operators in the code (n1), the total number of different operands in the code (n2), total operators in the code (N 1), and total operands in the code (N2). Finally, from these factors, we can compare their program level, effort, time to implement, program length and vocabulary. III. METHODS

In this study, we compare between five android open source audio players (based on various features). Android audio players should consist of the basic features like play a song, list of all songs etc. Extra features like the display of lyrics of the song, Graphical equalizer. Halstead defines the following metrics [3]: The length (N) of a program P is: N = N 1 + N2, where N1, N2 are total number of operators and operands respectively The vocabulary (n) of a program P is: n = n 1+ n2, where n1, n2 are unique operators and operands respectively Program volume (V) = N * log n Potential (minimal) Volume V* =(2+n 2)log2(2+n2) Implementation (program) Level, L=V*/V Program Difficulty, D=1/L Program Level Estimator L'= (2/n 1)*(n2/N2) Intelligence I=L*V Effort E=V/L Time T=E/S(Where S=18) M=Metrics Pi=Audio players.

978-1-5090-5681-1 /17/$31.00 ©2017 IEEE

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International Conference on Innovations in Power and Advanced Computing Technologies [i-PACT2017]

M n1 n2

N1 N2 N n

V

TABLE II: COMPARISON OF VARIOUS SOFTWARE METRICS P1

107 1k 4k

188k

148k

9717

8252

11k

172k

144k

6374

26447

1314

16.4k

P5

342

37k

151k

121k

6716

170k

2304k

1873k

2554k

1885k

0.072

0.0530

0.0543

0.0594

0.042

297

690

661

713

13.76

T

29k

11k

114k

D E

34k

8k

P4

533

139k

12.3k

I

9k

P3

389

13k

V* L

P2

444

2346k 130k

122k

18.85

43457k 2414k

101k

18.40

34493k 1916k

151k

16.83

42995k 2388k

80582 23.40

Figure 3. Effort Measure.

576

44129k 2451k

Figure 4: Operators and Operands Measure.

Figure. 1 Intelligence Measure.

Fig1 describes about Intelligence Measure.Fig2 describes about Time Measure .Fig3 describes about Effort Measure. Fig4 describes about the operators and operands measure. IV. CONCLUSION

In this study, we can observe that audio player1 takes 20 times more time when compared to other players. We can also notice from the above table II player1 completes with 50% of the effort when compared to the remaining players. V. RESULTS AND DISCUSSION

From the Table II, it is clear that player5 has high complexity and player1 has the low complexity than others. Hence the complexity of the software is directly proportional to the potential volume V*. ACKNOWLEDGMENT

Figure. 2 Time Measure.

We are thankful to Prof. Chandrasegar Thirumalai for providing an opportunity to write this paper and for guiding through every step from the beginning.

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REFERENCES

Al Qutaish, R. E., & Abran, A. (2005, September). An analysis of the

-Verlag . Dhillon, A., & Singh, A. (2012). Analysis of Software Metrics for Bubble Sort and Selection Sort. International Journal of Computer Applications & Information Technology. [3] Okeyinka, A. E., & Bamigbola, O. M. (2012). Numerical Study of Depth of Recursion in Complexity Measurement Using Halstead Measure. International Journal of Applied. [4] Kafura, D. and Reddy, G.R., 1987. The use of software complexity metrics in software maintenance. IEEE Transactions on Software Engineering. [5] Jensen, Howard A, K. Vairavan. "An experimental study of software metrics for real-time software." IEEE Trans. on Software Engineering. [6] https://github.com/SueSmith/android-music-player [7] https://github.com/googlesamples/android-UniversalMusicPlayer [8] https://en.wikipedia.org/wiki/Software_metric [9] https://github.com/psaravan/JamsMusicPlayer [10] https://github.com/alexdantas/kure-music-player [11] https://github.com/tomahawk-player/tomahawk-android [12] ications and Networking Technologies (ICCCNT) 2013, pp. 1-5 [13] [2]

conference on Electrical, Electronics, and Optimization Techniques, ICEEOT, IEEE & 978-1-4673-9939-5, March 2016. [14] P. Dhavachelvan, Chandra Segar T, K. Satheskumar, "Evaluation of

International Advance Computing (IACC), India, pp. 2325 2329, March 2009 [15] F. Fio -oriented [16]

Volume 53, Issue 2, 31 August 2000, Pages 111-136

Computer Science and Applications (TIJCSA) & India, TIJCSA Publishers & 2278-1080, Vol. 1, No 5 / pp. 1-7 / July 2012 [25] Vinothini S, Chandra Segar Thirumalai, Vijayaragavan R, Senthil

[26]

International Research Journal of Engineering and Technology (IRJET), Volume: 02 Issue: 02 May -2015

No. 2, May 2015, pp.373-382 [27] Chandrasegar

Issue 4, Dec. 2016, pp. 4907-4916. [28] Chandramowliswaran N, Srinivasan.S, and on Linear based Set Associative Cach of Computer Science and Engg. (IJCSE) & India, Engineering Journals & 0975-3397, Vol. 4 No. 08 / pp. 1383-1386 / Aug. 2012. [29] for Error Detection and Cor 2016, pp. 5009-5020. [30] Gupta, Madhuri, and Arvind Kalia. "Empirical Study of Software Metrics." Research Journal of Science and Technology 9.1 (2017): 1724. [31] Multi Key (MEMK) generation scheme for secure transportation of sensitive data [32] Bansal, Ankita. "Empirical analysis of search based algorithms to identify change prone classes of open source software." Computer Languages, Systems & Structures 47 (2017): 211-231. [33] -key distribution scheme using Diophantine IPACT 2017. [34] Delgado-Pérez, Pedro, et al. "GiGAn: Evolutionary Mutation Testing for C++ Object-Oriented Systems." mij 1 (2017): 1. [35] Asymmetric Cryptomata for Cloud Confidentiality and Blind Signature

E International journal of pharmacy and technology, Vol. 8 Issue 3, Sep. 2016, pp. 16296-16303 [17] Software metric Numerical Data analysis using Box plot and control chart methods, VIT University, DOI:10.13140/RG.2.2.27422.95041 [18] -Mail System and technology, Vol. 8 Issue 4, Dec. 2016, pp. 21797-21806. [19] Halstead Metric for Intelligence, Effort, Time predictions, DOI:10.13140/RG.2.2.17988.42881 [20] Vaishnavi B, Karthikeyan J, Kiran Yarrakula, Chandrasegar Thirumalai,

nd Communication Systems (ICECS), IEEE & 978-1-4673-7832-1, Feb. 2016 [21] E Malathy, Chandra Segar Thirumalai, "Review on non-linear set associative cache design," IJPT, Dec-2016, Vol. 8, Issue No.4, pp. 53205330 [22] Chandrasegar Thirumalai, Senthilkumar M, Silambarasan R, Carlos Issue 4, Dec. 2016, pp. 21869-21874. [23] Vinothini S, Chandra Segar Thirumalai, Vijayaragavan R, Senthil

Gowtham Reddy.K currently pursuing MS Software Engineering at VIT University, School of Information Technology, Vellore Campus, Vellore, India. T.D.V.Kiran currently pursuing MS Software Engineering at VIT University, School of Information Technology, Vellore Campus,Vellore,India.

International Research Journal of Engineering and Technology (IRJET), Volume: 02 Issue: 02 May -2015 [24] Chandramowliswaran N, Srinivasan.S, and

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