Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Analysis of Link Prediction in Directed and Weighted Social Network Structure Read chapter Read first chapter

2018 | OriginalPaper | Chapter

Temporal Modelling of Bug Numbers of Open Source Software Applications Using LSTM

Authors : Jayadeep Pati, Krishnkant Swarnkar, Gourav Dhakad, K. K. Shukla

Published in: Intelligent Systems Technologies and Applications

Publisher: Springer International Publishing

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Predicting the number of bugs in any software application is an important but challenging task. The software manager by modelling the bug numbers, can take timely decisions in reducing the amount of effort investment and also the allocation of resources. The software developers can also take effective steps for reducing the number of bugs in the future version of the software application. The end users also can make a timely decision on adoption of a particular software application by knowing the growth pattern of bugs in advance. The challenges behind modeling the bug growth patterns are random causes behind a bug. A bug in any software may be caused during testing, development or application. Causal modelling of bug numbers is a complex and tedious task as they consider many internal characteristics to be modelled. In this paper, we have used we have used Long Short Term Memory (LSTM) [14] Network for temporal modelling the bug numbers of three different software applications. We have used both univariate and multivariate modelling approach to predict bug number in advance. The goal is to have an appropriate model for software bug growth pattern.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter Automated Quiz Generator
next chapter Direct Demodulator for Amplitude Modulated Signals Using Artificial Neural Network
Literature
1.
Zimmermann, T., Nagappan, N., Zeller, A.: Predicting bugs from history. In: Software Evolution, pp. 69–88. Springer, Heidelberg (2008)
2.
Herraiz, I., Gonzalez-Barahona, J.M., Robles, G.: Forecasting the number of changes in Eclipse using time series analysis. In: Fourth International Workshop on ICSE Workshops on Mining Software Repositories, MSR 2007, p. 32. IEEE, May 2007
3.
Wu, W., Zhang, W., Yang, Y., Wang, Q.: Time series analysis for bug number prediction. In: 2010 2nd International Conference on Software Engineering and Data Mining (SEDM), pp. 589–596. IEEE, June 2010
4.
Zhang, H.: An initial study of the growth of eclipse defects. In: Proceedings of the 2008 International Working Conference on Mining Software Repositories, pp. 141–144. ACM, May 2008
5.
Pati, J., Shukla, K.K.: A nonlinear ARIMA technique for Debian bug number prediction. In: Proceedings of the International Conference on Advances in Computer and Electronics Technology - ACET 2014, SEEK-DL 2014, September 2014. doi:10.​15224/​978-1-63248-024-8-14
6.
Pati, J., Shukla, K.K.: A comparison of ARIMA, neural network and a hybrid technique for Debian bug number prediction. In: 2014 International Conference on Computer and Communication Technology (ICCCT), pp. 47–53. IEEE, September 2014
7.
8.
9.
10.
Zhao, X., Wang, C., Yang, Z., Zhang, Y., Yuan, X.: Online news emotion prediction with bidirectional LSTM. In: International Conference on Web-Age Information Management, pp. 238–250. Springer International Publishing, June 2016
11.
12.
Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: LSTM: a search space odyssey. IEEE Trans. Neural Netw. Learn. Syst. (2016)
13.
Gers, F.A., Schmidhuber, J., Cummins, F.: Learning to forget: continual prediction with LSTM. Neural Comput. 12(10), 2451–2471 (2000)CrossRef
14.
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)CrossRef
15.
Graves, A.: Supervised sequence labelling. In: Supervised Sequence Labelling with Recurrent Neural Networks, pp. 5–13. Springer, Heidelberg (2012)
16.
Metadata
Title
Temporal Modelling of Bug Numbers of Open Source Software Applications Using LSTM
Authors
Jayadeep Pati
Krishnkant Swarnkar
Gourav Dhakad
K. K. Shukla
Copyright Year
2018
Book
Intelligent Systems Technologies and Applications

Print ISBN: 978-3-319-68384-3

Electronic ISBN: 978-3-319-68385-0

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-68385-0_16

Premium Partner

    Image Credits