Room temperature femtosecond optical parametric generation in MgO-doped stoichiometric LiTaO3

We demonstrate room temperature femtosecond optical parametric generation with high average output power in periodically poled MgO-doped stoichiometric LiTaO3. Direct pumping with 725-fs pulses from a passively mode-locked thin disk laser at 1030nm resulted in stable 1.5W average signal power at 1484nm at the full laser repetition rate of 59MHz. With this demonstration we achieved a significant simplification of our recently presented red-green-blue laser source because no temperature stabilization of any nonlinear crystal is require

Software defect prediction: do different classifiers find the same defects?

Open Access: This article is distributed under the terms of the Creative Commons Attribution 4.0 International License CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.During the last 10 years, hundreds of different defect prediction models have been published. The performance of the classifiers used in these models is reported to be similar with models rarely performing above the predictive performance ceiling of about 80% recall. We investigate the individual defects that four classifiers predict and analyse the level of prediction uncertainty produced by these classifiers. We perform a sensitivity analysis to compare the performance of Random Forest, Naïve Bayes, RPart and SVM classifiers when predicting defects in NASA, open source and commercial datasets. The defect predictions that each classifier makes is captured in a confusion matrix and the prediction uncertainty of each classifier is compared. Despite similar predictive performance values for these four classifiers, each detects different sets of defects. Some classifiers are more consistent in predicting defects than others. Our results confirm that a unique subset of defects can be detected by specific classifiers. However, while some classifiers are consistent in the predictions they make, other classifiers vary in their predictions. Given our results, we conclude that classifier ensembles with decision-making strategies not based on majority voting are likely to perform best in defect prediction.Peer reviewedFinal Published versio

Exploratory Comparison of Expert and Novice Pair Programmers

We conducted quasi-experiment comparing novice pair programmers to expert pair programmers. The expert pairs wrote tests with a higher instruction, line, and method coverage than the novice pairs and changed the given program skeleton to a larger extent. However, the expert pairs were also slower than the novice pairs. The pairs within both groups switched keyboard and mouse possession frequently. Furthermore, most pairs did not share the input devices equally but rather had one partner who is more active than the other

Comparing Static and Dynamic Weighted Software Coupling Metrics

Coupling metrics that count the number of inter-module connections in a software system are an established way to measure internal software quality with respect to modularity. In addition to static metrics, which are obtained from the source or compiled code of a program, dynamic metrics use runtime data gathered, e.g., by monitoring a system in production. Dynamic metrics have been used to improve the accuracy of static metrics for object-oriented software. We study weighted dynamic coupling that takes into account how often a connection (e.g., a method call) is executed during a system’s run. We investigate the correlation between dynamic weighted metrics and their static counterparts. To compare the different metrics, we use data collected from four different experiments, each monitoring production use of a commercial software system over a period of four weeks. We observe an unexpected level of correlation between the static and the weighted dynamic case as well as revealing differences between class- and package-level analyses

Empirical evaluation of the effects of experience on code quality and programmer productivity: an exploratory study

There is a widespread belief in both SE and other branches of science that experience helps professionals to improve their performance. However, cases have been reported where experience not only does not have a positive influence but sometimes even degrades the performance of professionals