DOI: https://doi.org/10.21456/vol8iss1pp76-83

Peningkatan Akurasi Prediksi Waktu Perbaikan Bug dengan Pendekatan Partisi Data

*Mochammad Arief Ridwan  -  Institut Teknologi Sepuluh Nopember, Indonesia
Siti Rochimah  -  Institut Teknologi Sepuluh Nopember, Indonesia
Open Access Copyright (c) 2018 JSINBIS (Jurnal Sistem Informasi Bisnis)

Citation Format:
Abstract
Software developers need to have a plan in setting up software development costs. Software repairs in the system maintenance phase can be caused by bugs. Bugs are malfunctions that occur in software that does not meet the needs of the software. The software bug can have a fast or long time in the repair depending on the difficulty level. Developers can be assisted by predictive model recommendations and provide time-out considerations for bug fixes. Some research has been done on the predicted time of bug fixes using various existing classification algorithms with free datasets that can be accessed or downloaded from the software site. The classification of existing research uses several datasets of varying time ranges, the results obtained from a very variable time span are assessed to be further enhanced by partitioning over time ranges prior to classification. With partitions based on the repair timeframe, improved accuracy has been made with some classification methods. The results obtained after performing trials on multiple datasets are an increase for the majority of the datasets used. There is also a decrease in accuracy in some tests performed with a particular dataset.
Fulltext View|Download
Keywords: Prediction; Bug, Fixing time; Partition

Article Metrics:

Article Info
Section: Research Articles
Language : ID-ID
Recent articles
Peningkatan Akurasi Prediksi Waktu Perbaikan Bug dengan Pendekatan Partisi Data Pengukuran Keberhasilan E-Learning dengan Mengadopsi Model Delone & Mclean Identifikasi Huruf Kapital Tulisan Tangan Menggunakan Linear Discriminant Analysis dan Euclidean Distance More recent articles
Most cited articles
Implementasi Sistem Pakar Diagnosis Penyakit Diabetes Mellitus Menggunakan Metode Fuzzy Logic Berbasis Web Metode Quality Function Deployment Dan Fuzzy Topsis Untuk Sistem Pendukung Keputusan Pemilihan Perusahaan Penyedia Jasa Internet Performance Dashboard pada Rumah Sakit Islam Algoritma K-Means Clustering Untuk Pengelompokan Ayat Al Quran Pada Terjemahan Bahasa Indonesia Studi Pengamanan Login Pada Sistem Informasi Akademik Menggunakan Otentifikasi One Time Password Berbasisis SMS dengan Hash MD5 More cited articles
  1. Nur, F.A., Siti, R., 2016. Memprediksi Waktu Perbaikan Bug dari Laporan Bug Menggunakan Klasifikasi Random Forest. Jurnal Sistem dan Informatika 11 (2), 1
  2. Norvig, P., Russel, S., 2010. Artificial Intelligence a Modern Approach. Pearson, New Jersey, MA
  3. Jiawei, H., Kamber, Micheline, 2006. Data Mining: Concepts and Technique. Elsevier, Waltham, MA
  4. Gall, H.C., Giger, E., Pinzger, M., 2010. Predicting the fix time of bugs, Proceedings of the 2nd International Workshop on Recommendation System for Software Engineering, Cape Town, May 4, 52-56
  5. Abdelmoez, W., Elsalmy, F.M., Kholief, M., 2013. Bug fix-time prediction model using naïve bayes classifier. International Conference on Computer Theory and Application, Alexandria, October 13, 167-172
  6. Alenezi, M., Banitaan, S., 2013. Bug reports prioritization : Which features and classifier to use? International Conference on Machine Learning and Applications, Miami, April 4, 112-116
  7. Gong, L., Versteeg, S., Zhang, H., 2013. Predicting bug fixing-time: an empirical study of commercial software projects. Proceedings of the 2013 Internatioanal Conference on Software Engineering, San Francisco, May 18, 1042-1051
  8. Romi, S. W., 2016. A Systematic Literature Review of Software Defect Prediction: Research Trends, Dataset, Methods and Frameworks. Journal of Software Engineering, 1 (2), 1-16
  9. Breiman, L., 2001. Random Forest, Machine Learning, 45 (1), 5-32
  10. Bhuwaneswari, V., Vijayakumar, K., 2014. How much effort needed to fix the bug? A data mining approach for effort estimation and analysing of bug report attributes in Firefox. International Conference on Intelligent Computing Applications, Coimbatore, March 6, 335-339

Last update:

No citation recorded.

Last update: 2024-12-26 00:08:05

No citation recorded.