Practical approaches to speech coding
Practical approaches to speech coding
Fundamentals of speech recognition
Fundamentals of speech recognition
Algorithmic cost estimation for software evolution
Proceedings of the 22nd international conference on Software engineering
A Vector-Based Approach to Software Size Measurement and Effort Estimation
IEEE Transactions on Software Engineering
Software Engineering Economics
Software Engineering Economics
Linear Prediction of Speech
IEEE Software
Distance Measures for Effective Clustering of ARIMA Time-Series
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Understanding and predicting effort in software projects
Proceedings of the 25th International Conference on Software Engineering
On-line signature verification using LPC cepstrum and neural networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Journal of Software Maintenance and Evolution: Research and Practice
Classification of speech dysfluencies with MFCC and LPCC features
Expert Systems with Applications: An International Journal
Classification of Speech Dysfluencies Using LPC Based Parameterization Techniques
Journal of Medical Systems
Perceptive analysis of query by singing system through query excerption
Proceedings of the Second International Conference on Computational Science, Engineering and Information Technology
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This paper presents an approach to recover time variant information from software repositories. It is widely accepted that software evolves due to factors such as defect removal, market opportunity or adding new features. Software evolution details are stored in software repositories which often contain the changes history. On the other hand there is a lack of approaches, technologies and methods to efficiently extract and represent time dependent information. Disciplines such as signal and image processing or speech recognition adopt frequency domain representations to mitigate differences of signals evolving in time. Inspired by time-frequency duality, this paper proposes the use of Linear Predictive Coding (LPC) and Cepstrum coefficients to model time varying software artifact histories. LPC or Cepstrum allow obtaining very compact representations with linear complexity. These representations can be used to highlight components and artifacts evolved in the same way or with very similar evolution patterns. To assess the proposed approach we applied LPC and Cepstral analysis to 211 Linux kernel releases (i.e., from 1.0 to 1.3.100), to identify files with very similar size histories. The approach, the preliminary results and the lesson learned are presented in this paper.