Analisa Studi Empirik Kerangka Kerja Pengukuran Kualitas Perangkat Lunak Bebas Cacat

Agus Pamuji


Testing activitiy is a strategic step to determine software quality was generated,  so that is accepted by the end user. In the testing an errors  were found that may be cause to risk a defect on the software. This study was conducted by establishing a measurement framework to analyze software metrics test toward risk prediction of defects consisting of defect density, defect removal, and Line of code. In the analysis, the data set contains 53 module samples through a statistical approach with correlation analysis techniques. Based on the hypothesis were proposed, that there are only 2 of 3 items is received and shows a high significance of defect density and removal of defects towards software quality measurement.

Full Text:


V. Pham and M. Böhme, 2016, September. Model-Based Whitebox Fuzzing for Program Binaries. ASE 2016 Proceedings of the 31st ACM International Conference on Automated Software Engineering, 2016. on ( pp. 552–562). ACM.

K. M. R, T. Nadu, and S. G. Jacob, 2016. Improved Random Forest Algorithm for Software Defect Prediction through Data Mining Techniques, International Journal of Computer Applications. 117(23), pp. 18–22.

M. Kakkar and M. Kakkar, 2016, January. Feature selection in software defect prediction : A comparative study Feature Selection in Software Defect Prediction : A Comparative Study. 2016 6th International Conference - Cloud System and Big Data Engineering (Confluence) on (pp. 658-663).IEEE.

R. S. Pressman,2010. Software Engineering;A Practitiner’s Approach. McGrawHill.

C. Nagar and A. Dixit, 2011. Software Efforts and Cost Estimation with a Systematic Approach. CIS Journal.. 2(7), pp. 312–316.

Y. Singh, 2012. Software Testing. Cambridge Press .

E. A. Felix and S. P. Lee, 2017. “Integrated Approach to Software Defect Prediction,” IEEE Access, 3536(c) pp. 1–2.

N. Mandhan, D. K. Verma, and S. Kumar, 2015. Analysis of approach for predicting software defect density using static metrics,” Int. Conf. Comput. Commun. Autom., pp. 880–886.

V. Chauhan and D. L. Gupta, “Chauhan and Gupta A Comparative Analysis of DIT over MVG to Improve Quality of Software,” no. 1, pp. 3–16.

Q. Cao, Q. Sun, Q. Cao, and H. Tan,2015. Software defect prediction via transfer learning based neural network. 2015 First Int. Conf. Reliab. Syst. Eng. (ICRSE),IEEE, pp. 1–10.

E. Irawan et al.,2015. Penggunaan Random Under Sampling untuk Penanganan Ketidakseimbangan Kelas pada Prediksi Cacat Software Berbasis Neural Network, 1(2), pp. 92–100.

R. Malhotra, N. Pritam, and Y. Singh, 2014, .On the applicability of evolutionary computation for software defect prediction, 2014 International Conference on Advances in Computing,Communications and Informatics (ICACCI), 2014 pp. 2249–2257, IEEE.

R. Malhotra,2016. Empirical Research in Software Engineering. CRC Press.

P. Mohagheghi, R. Conradi, O. M. Killi, and H. Schwarz, 2004, May. An empirical study of software reuse vs. defect-density and stability, Proceedings of the 26th International Conference on Software Engineering (ICSE’04). pp. 282–291.

S. Kaur, S. Assistant, J. Kaur, S. Faculty, N. Chandigarh, and S. Singh, 2013. Effect of Data Preprocessing on Software Effort Estimation, International Journal Computation Application, 69( 25), pp. 975–8887,

S. Kumaresh and R. Baskaran, 2012, April. Experimental design on defect analysis in software process improvement, International Conference on Recent Advances in Computing and Software Systems. pp. 293–298.IEEE

G. K. Armah, G. Luo, and K. Qin, 2013, November. Multi_level data pre_processing for software defect prediction, 6th International Conference on Information Management, Innovation Management and Industrial Engineering . pp. 170–174.IEEE

F. Varanini, G. M. Hill, and W. Curlee, 2015. Simple Statistical Methods For Software Engineering Projects And Complexity.McGrawHill.

K. Sobhana, “Software Reliability Growth Model on Burr Type III-An Order Statistics Approach,” pp. 57–68.

W. E. Saris and I. N. Gallhofer, 2007.Design, Evaluation, and Analysis for Questionnaire for Survey Research.Wiley..

Davis. C,2013. SPSS for Applied Sciences_ Basic Statistical Testing.CSIRO Publishing.



  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Terindeks oleh :







Program Studi D4 Teknik Informatika
Politeknik Harapan Bersama Tegal
Jl. Mataram No.09 Pesurungan Lor Kota Tegal

Telp. +62283 - 352000

Email :


Copyright: JPIT (Jurnal Informatika: Jurnal Pengembangan IT) p-ISSN: 2477-5126 (print), e-ISSN 2548-9356 (online) 

Flag Counter
View Visitor Statistic


Creative Commons License
JPIT (Jurnal Informatika: Jurnal Pengembangan IT) is licensed under a Creative Commons Attribution 4.0 International License.