Search

Do open source software projects provide and maintain tests? What metrics are correlated with the test success? This paper answers these questions by executing tests of 452 open source software projects in GitHub and measuring 13 metrics from 77 projects. Only 117 projects passed all test cases. Additionally, the results are correlated with the comment density, public documented API density, and test coverage., PROFES 2014 : Product-Focused Software Process Improvement, December 10-12, 2014, Helsinki, Finland

Previous machine learning (ML) system development research suggests that emerging software quality attributes are a concern due to the probabilistic behavior of ML systems. Assuming that detailed development processes depend on individual developers and are not discussed in detail. To help developers to standardize their ML system development processes, we conduct a preliminary systematic literature review on ML system development processes. A search query of 2358 papers identified 7 papers as well as two other papers determined in an ad-hoc review. Our findings include emphasized phases in ML system developments, frequently described practices and tailored traditional software development practices.

Do open source software projects provide and maintain tests? What metrics are correlated with the test success? This paper answers these questions by executing tests of 452 open source software projects in GitHub and measuring 13 metrics from 77 projects. Only 117 projects passed all test cases. Additionally, the results are correlated with the comment density, public documented API density, and test coverage.

Do open source software projects provide and maintain tests? What metrics are correlated with the test success? This paper answers these questions by executing tests of 452 open source software projects in GitHub and measuring 13 metrics from 77 projects. Only 117 projects passed all test cases. Additionally, the results are correlated with the comment density, public documented API density, and test coverage., conference paper

To evaluate spatial variability of nitrous oxide (N2O) emissions and to elucidate their determining factors on a field-scale basis, N2O fluxes and various soil properties were evaluated in a 100- x 100-m onion (Allium cepa L.) field. Nitrous oxide fluxes were determined by a closed chamber method from the one-hundred 10- x 10-m plots. Physical (e.g., bulk density and water content), chemical (e.g., total N and pH), and biological (e.g., microbial biomass C and N) properties were determined from surface soil samples (0–0.1 m) of each plot. Geostatistical analysis was performed to examine spatial variability of both N2O fluxes and soil properties. Multivariate analysis was also conducted to elucidate relationships between soil properties and observed fluxes. Nitrous oxide fluxes were highly variable (average 331 µg N m-2 h-1, CV 217%) and were log–normally distributed. Log-transformed N2O fluxes had moderate spatial dependence with a range of >75 m. High N2O fluxes were observed at sites with relatively low elevation. Multivariate analysis indicated that an organic matter factor and a pH factor of the principal component analysis were the main soil-related determining factors of log-transformed N2O fluxes. By combining multivariate analysis with geostatistics, a map of predicted N2O fluxes closely matched the spatial pattern of measured fluxes. The regression equation based on the soil properties explained 56% of the spatially structured variation of the log-transformed N2O fluxes. Site-specific management to regulate organic matter content and water status of a soil could be a promising means of reducing N2O emissions from agricultural fields.

To evaluate spatial variability of nitrous oxide (N2O) emissions and to elucidate their determining factors on a field-scale basis, N2O fluxes and various soil properties were evaluated in a 100- x 100-m onion (Allium cepa L.) field. Nitrous oxide fluxes were determined by a closed chamber method from the one-hundred 10- x 10-m plots. Physical (e.g., bulk density and water content), chemical (e.g., total N and pH), and biological (e.g., microbial biomass C and N) properties were determined from surface soil samples (0–0.1 m) of each plot. Geostatistical analysis was performed to examine spatial variability of both N2O fluxes and soil properties. Multivariate analysis was also conducted to elucidate relationships between soil properties and observed fluxes. Nitrous oxide fluxes were highly variable (average 331 µg N m-2 h-1, CV 217%) and were log–normally distributed. Log-transformed N2O fluxes had moderate spatial dependence with a range of >75 m. High N2O fluxes were observed at sites with relatively low elevation. Multivariate analysis indicated that an organic matter factor and a pH factor of the principal component analysis were the main soil-related determining factors of log-transformed N2O fluxes. By combining multivariate analysis with geostatistics, a map of predicted N2O fluxes closely matched the spatial pattern of measured fluxes. The regression equation based on the soil properties explained 56% of the spatially structured variation of the log-transformed N2O fluxes. Site-specific management to regulate organic matter content and water status of a soil could be a promising means of reducing N2O emissions from agricultural fields.

To evaluate spatial variability of nitrous oxide (N2O) emissions and to elucidate their determining factors on a field-scale basis, N2O fluxes and various soil properties were evaluated in a 100- x 100-m onion (Allium cepa L.) field. Nitrous oxide fluxes were determined by a closed chamber method from the one-hundred 10- x 10-m plots. Physical (e.g., bulk density and water content), chemical (e.g., total N and pH), and biological (e.g., microbial biomass C and N) properties were determined from surface soil samples (0–0.1 m) of each plot. Geostatistical analysis was performed to examine spatial variability of both N2O fluxes and soil properties. Multivariate analysis was also conducted to elucidate relationships between soil properties and observed fluxes. Nitrous oxide fluxes were highly variable (average 331 µg N m-2 h-1, CV 217%) and were log–normally distributed. Log-transformed N2O fluxes had moderate spatial dependence with a range of >75 m. High N2O fluxes were observed at sites with relatively low elevation. Multivariate analysis indicated that an organic matter factor and a pH factor of the principal component analysis were the main soil-related determining factors of log-transformed N2O fluxes. By combining multivariate analysis with geostatistics, a map of predicted N2O fluxes closely matched the spatial pattern of measured fluxes. The regression equation based on the soil properties explained 56% of the spatially structured variation of the log-transformed N2O fluxes. Site-specific management to regulate organic matter content and water status of a soil could be a promising means of reducing N2O emissions from agricultural fields.