Towards Effective Bug Triage with Software Data Reduction Techniques

International audienceSoftware companies spend over 45 percent of cost in dealing with software bugs. An inevitable step of fixing bugs is bug triage, which aims to correctly assign a developer to a new bug. To decrease the time cost in manual work, text classification techniques are applied to conduct automatic bug triage. In this paper, we address the problem of data reduction for bug triage, i.e., how to reduce the scale and improve the quality of bug data. We combine instance selection with feature selection to simultaneously reduce data scale on the bug dimension and the word dimension. To determine the order of applying instance selection and feature selection, we extract attributes from historical bug data sets and build a predictive model for a new bug data set. We empirically investigate the performance of data reduction on totally 600,000 bug reports of two large open source projects, namely Eclipse and Mozilla. The results show that our data reduction can effectively reduce the data scale and improve the accuracy of bug triage. Our work provides an approach to leveraging techniques on data processing to form reduced and high-quality bug data in software development and maintenance

CREATING BUG REPOSITORY IN THE FIELD OF SOFTWARE INDUSTRY

An unavoidable step of fixing bugs is bug triage, which aims to properly assign a developer to a different bug. We combine instance selection with feature selection to concurrently reduce data scale around the bug dimension and also the word dimension. To lower time cost in manual work, text classification techniques are put on conduct automatic bug triage. Within this paper, we address the issue of information reduction for bug triage, i.e., how you can lessen the scale and improve the caliber of bug data. Software companies spend over 45 percent of cost in working with software bugs. To look for the order of applying instance selection and have selection, we extract attributes from historic bug data sets and make a predictive model for any new bug data set. The outcomes reveal that our data reduction can effectively lessen the data scale and enhance the precision of bug triage. Our work provides a technique for leveraging techniques on information systems to create reduced and-quality bug data in software development and maintenance. We empirically investigate performance of information reduction on totally 600,000 bug reports of two large free projects, namely Eclipse and Mozilla

HIGH-QUALITY AIMING CLASSIFICATION TO CONSIGN BUGS

To lower time cost in manual work, text classification techniques are put on conduct automatic bug triage. Within this paper, we address the issue of information reduction for bug triage, i.e., how you can lessen the scale and improve the caliber of bug data. Software companies spend over 45 percent of cost in working with software bugs. An unavoidable step of fixing bugs is bug triage, which aims to properly assign a developer to a different bug. We combine instance selection with feature selection to concurrently reduce data scale around the bug dimension and also the word dimension. To look for the order of using instance selection and have selection, we extract characteristics from historic bug data sets and make a predictive model for any new bug data set. Our work provides a technique for leveraging techniques on information systems to create reduced and-quality bug data in software development and maintenance. We empirically investigate performance of information reduction on totally 600,000 bug reviews of two large free projects, namely Eclipse and Mozilla. The outcomes reveal that our data reduction can effectively lessen the data scale and enhance the precision of bug triage

CONCERNING EFFECTIVE ERROR IDENTIFIED WITH SOFTWARE RECORDS DECREASE TECHNIQUES

To reduce time cost in manual work, text classification techniques they can fit on conduct automatic bug triage. In this particular paper, we address the problem of understanding reduction for bug triage, i.e., the simplest way to reduce the scale and improve the grade of bug data. Software companies spend over 45 percent of cost when controlling software bugs. An inevitable step of fixing bugs is bug triage, which aims to correctly assign a developer to a new bug. To discover a purchase of applying instance selection and possess selection, we extract attributes from historic bug data sets developing a predictive model for every new bug data set. We combine instance selection with feature selection to concurrently reduce data scale inside the bug dimension coupled with word dimension. The conclusion result shows our data reduction can effectively reduce the data scale and lift a realistic look at bug triage. We empirically investigate performance of understanding reduction on totally 600,000 bug reports of two large free projects, namely Eclipse and Mozilla. Our work supplies a kinds of leveraging techniques on human sources to produce reduced and-quality bug data in software development and maintenance

NEAR OPERATIVE VIRUSTOAST WITH SOFTWARE DATA DISCOUNT METHODS

To lessen time cost in manual work, text classification techniques they can fit on conduct automatic bug triage. Within this paper, we address the issue of understanding reduction for bug triage, i.e., the easiest method to reduce the scale and improve the standard of bug data. Software companies spend over 45 percent of cost when controlling software bugs. An unavoidable step of fixing bugs is bug triage, which aims to properly assign a developer to a different bug. To uncover an order of applying instance selection and possess selection, we extract attributes from historic bug data sets creating a predictive model for each new bug data set. We combine instance selection with feature selection to concurrently reduce data scale within the bug dimension along with word dimension. To conclude result shows our data reduction can effectively reduce the data scale and lift take a look at bug triage. We empirically investigate performance of understanding reduction on totally 600,000 bug reports of two large free projects, namely Eclipse and Mozilla. Our work supplies a types of leveraging techniques on human sources to create reduced and-quality bug data in software development and maintenance

Carbon Footprint Assessment and Mitigation Options of Dairy under Chinese Conditions

With the rapid human population growth and economic development, demand for animal products continues to increase and livestock production rapidly expands. Greenhouse gases (GHG) emission from livestock research 7.52 billion tons CO2-eq per year, accounting for 50% of agricultural emissions and 18% of global anthropogenic GHG emissions (FAO, 2014), making it become an important source of GHG emissions. The Chinese livestock production emits 373 GHG of million tons CO2-eq. Methane (CH4) emitted from enteric fermentation is 10.74 million tons (equivalent to 225.6 million tons CO2-eq), accounting for 60.7% of total livestock GHG emissions. CH4 emitted from manure management is 3.33 million tons (equivalent to 69.9 million tons CO2-eq), accounting for 18.9% of total livestock GHG emissions. Nitrous oxide (N2O) emitted from manure management is 0.25 million tons (equivalent to 77.2 million tons CO2-eq), accounted for 20.4% of the total livestock GHG emissions (MEE, 2018). The enteric fermentation and manure management contribute 40% to agricultural GHG emissions. Expansion of livestock production results in high demand of feedstuffs, bringing greater pressure on natural resources. It is of particular concern that the livestock sector has already been a major user of natural resources. For example, approximately 35% of total cropland and 20% of green water have been used for animal feed production (Opio et al., 2013). Feed-related emissions represent about half of total emissions from livestock supply chains (Gerber et al., 2013). Therefore, it is very important to evaluate GHG emissions from the whole life cycle of livestock production. Besides improved manure utilization and water usage efficiency, management of carbon emissions and carbon footprint is highlighted as an important research topic. This project is expected to identify and execute appropriate interventions for reducing carbon footprint and economic cost of dairy production

A review of microgrid development in the United States – A decade of progress on policies, demonstrations, controls, and software tools

Microgrids have become increasingly popular in the United States. Supported by favorable federal and local policies, microgrid projects can provide greater energy stability and resilience within a project site or community. This paper reviews major federal, state, and utility-level policies driving microgrid development in the United States. Representative U.S. demonstration projects are selected and their technical characteristics and non-technical features are introduced. The paper discusses trends in the technology development of microgrid systems as well as microgrid control methods and interactions within the electricity market. Software tools for microgrid design, planning, and performance analysis are illustrated with each tool's core capability. Finally, the paper summarizes the successes and lessons learned during the recent expansion of the U.S. microgrid industry that may serve as a reference for other countries developing their own microgrid industries

Climate Resilient & Equitable Water Systems Capital Scan

Climate change is affecting water supply, water management and the health of communities in U.S. cities. Changes in the timing, frequency and intensity of precipitation are placing stress on the built and natural systems that provide fresh water, manage storm water, and treat wastewater. Droughts are shrinking the water supply; heavy rainfall overburdens storm water systems, causing flooding in homes and neighborhoods. Low-income people and communities of color are often the most vulnerable to climate change, living in low-lying areas and lacking the resources to adapt and cope with challenges associated with these patterns.The cumulative impact of climate change on water resources not only leads to a reduction in water quality and the destruction of homes and property, but it can also be a threat to public health, force relocation of communities and cause economic harm.The vision of Kresge's Environment Program is to help communities build resilience in the face of climate change. We believe that cities are central to action on climate change and equity must be a fundamental part of our work in climate adaptation, climate mitigation and building social cohesion