Mining recurrent activities: Fourier analysis of change events
Abram Hindle, Michael W. Godfrey, Richard C. Holt · 2009
Within the field of software repository mining, it is common practice to extract change-events from source control systems and then abstract these events to allow for different analyses. One approach is to apply time-series analysis by aggregating these events into signals. Time-series analysis requires that researchers specify a period of study; usually ldquonaturalrdquo periods such as days, months, and years are chosen. As yet there has been no research to validate that these assumptions are reasonable. We address this by applying Fourier analysis to discover the ldquonaturalrdquo periodicities of software development. Fourier analysis can detect and determine the periodicity of repeating events. Fourier transforms represent signals as linear combinations of sine-waves that suggest how much activity occurs at certain frequencies. If behaviors of different frequencies are mixed into one signal, they can be separated. Thus Fourier transforms can help us identify significant development process sub-signals within software projects.