Defect Bash - Literature Review
Defect bash is a co-located testing session performed by a group of people. We performed a systematic review of the academic and grey literature, i.e. informally published writings, of the defect bash. Altogether, we found 44 items (17 academic and 27 grey literature sources) that were identified useful for the review. Based on the review the definition of defect bash is presented, benefits and limitations of using defect bash are given. Finally, the process of doing defect bash is outlined. This review provides initial understanding on how defect bash could be useful in achieving the software quality and lays foundation for further academic studies of this topic.
Microdynamics of Process Evolution
Presents the seven basic steps of opportunistic software process evolution. (1) Notice problems in products or outcomes. (2) Choose an important or chronic problem and look for a way to solve it, in whole or in part. (3) Conceive of a new, borrowed or modified process that could solve the problem at an acceptable cost and in an acceptable time frame. (4) Try the new process on a real project. (5) Adjust the process in light of experience and in light of the new problems that were created by the process. (6) If the solution is worth perpetuating, look for a way to make it more efficient or durable by adding infrastructure (such as documentation, training or tool support). Otherwise, try to understand why it doesn't seem to work. (7) Return to step 1. Pay attention to projects on the scale of hours, cubicles, people and episodes. In complex cognitive processes like software development, if process evolution is foiled on the micro scale, it will be foiled utterly.
Surface roughness monitoring by singular spectrum analysis of vibration signals
Abstract This study assessed two methods for enhanced surface roughness (Ra) monitoring based on the application of singular spectrum analysis (SSA) to vibrations signals generated in workpiece-cutting tool interaction in CNC finish turning operations i.e., the individual analysis of principal components (I-SSA), and the grouping analysis of correlated principal components (G-SSA). Singular spectrum analysis is a non-parametric technique of time series analysis that decomposes a signal into a set of independent additive time series referred to as principal components. A number of experiments with different cutting conditions were performed to assess surface roughness monitoring using both of these methods. The results show that singular spectrum analysis of vibration signal processing discriminated the frequency ranges effective for predicting surface roughness. Grouping analysis of correlated principal components (G-SSA) proved to be the most efficient method for monitoring surface roughness, with optimum prediction and reliability results at a lower analytical-computational cost. Finally, the results show that singular spectrum analysis is an ideal method for analyzing vibration signals applied to the on-line monitoring of surface roughness.