6,372 research outputs found
DeepSoft: A vision for a deep model of software
Although software analytics has experienced rapid growth as a research area,
it has not yet reached its full potential for wide industrial adoption. Most of
the existing work in software analytics still relies heavily on costly manual
feature engineering processes, and they mainly address the traditional
classification problems, as opposed to predicting future events. We present a
vision for \emph{DeepSoft}, an \emph{end-to-end} generic framework for modeling
software and its development process to predict future risks and recommend
interventions. DeepSoft, partly inspired by human memory, is built upon the
powerful deep learning-based Long Short Term Memory architecture that is
capable of learning long-term temporal dependencies that occur in software
evolution. Such deep learned patterns of software can be used to address a
range of challenging problems such as code and task recommendation and
prediction. DeepSoft provides a new approach for research into modeling of
source code, risk prediction and mitigation, developer modeling, and
automatically generating code patches from bug reports.Comment: FSE 201
Mapping e-Scienceâs Path in the Collaboration Space: Ontological Approach to Monitoring Infrastructure Development
In an undertaking such as the U.S. Cyberinfrastructure Initiative, or the UK e-science program, which span many years and comprise a great many projects funded by multiple agencies, it can be very difficult to keep tabs on what everyone is doing. But, it is not impossible. In this paper, we propose the construction of ontologies as a means of monitoring a research programâs portfolio of projects. In particular, we introduce the âvirtual laboratory ontologyâ (VLO) and show how its application to e-Science yields a mapping of the distribution of projects in several dimensions of the âcollaboration space.â In this paper, we sketch out a method to induce a project mapping from project descriptions and present the resulting map for the UK e-science program. This paper shows the proposed mapping approach to be informative as well as feasible, and we expect that its further development can prove to be substantively useful for future work in cyber-infrastructure-building.e-Science, virtual laboratory ontology, collaboration space, project mapping, cyber-infrastructure building
Towards Autonomous Aviation Operations: What Can We Learn from Other Areas of Automation?
Rapid advances in automation has disrupted and transformed several industries in the past 25 years. Automation has evolved from regulation and control of simple systems like controlling the temperature in a room to the autonomous control of complex systems involving network of systems. The reason for automation varies from industry to industry depending on the complexity and benefits resulting from increased levels of automation. Automation may be needed to either reduce costs or deal with hazardous environment or make real-time decisions without the availability of humans. Space autonomy, Internet, robotic vehicles, intelligent systems, wireless networks and power systems provide successful examples of various levels of automation. NASA is conducting research in autonomy and developing plans to increase the levels of automation in aviation operations. This paper provides a brief review of levels of automation, previous efforts to increase levels of automation in aviation operations and current level of automation in the various tasks involved in aviation operations. It develops a methodology to assess the research and development in modeling, sensing and actuation needed to advance the level of automation and the benefits associated with higher levels of automation. Section II describes provides an overview of automation and previous attempts at automation in aviation. Section III provides the role of automation and lessons learned in Space Autonomy. Section IV describes the success of automation in Intelligent Transportation Systems. Section V provides a comparison between the development of automation in other areas and the needs of aviation. Section VI provides an approach to achieve increased automation in aviation operations based on the progress in other areas. The final paper will provide a detailed analysis of the benefits of increased automation for the Traffic Flow Management (TFM) function in aviation operations
Prediction of a Sprint Deliverys Capabilities in Iterative-based Software Development
Iterative-based software development has been frequently implemented in working environment. A modern era software project demands that the product is delivered on every sprint development. Hence, the execution of a sprint requires ample supervision and capabilities to deliver a high quality product at the end of the software project development. This researchs purpose is to give support for a software projects supervisor or owner in predicting the end products capability by knowing the performance level of each sprint. The method proposed for this purpose is to build a prediction model utilizing a number of features in a form of characteristics from a dataset containing software project iterations. The proposed model is built using Random Forest Regressor as a main method, with KNN (K-Nearest Neighbors) and Decision Tree Regressor being the comparison methods. Testing results show that compared to KNN and Decision Tree, Random Forest Regressor yields the best performance through its steady results on every stage progression of all tested software projects
Prediction of a Sprint Deliverys Capabilities in Iterative-based Software Development
Iterative-based software development has been frequently implemented in working environment. A modern era software project demands that the product is delivered on every sprint development. Hence, the execution of a sprint requires ample supervision and capabilities to deliver a high quality product at the end of the software project development. This researchs purpose is to give support for a software projects supervisor or owner in predicting the end products capability by knowing the performance level of each sprint. The method proposed for this purpose is to build a prediction model utilizing a number of features in a form of characteristics from a dataset containing software project iterations. The proposed model is built using Random Forest Regressor as a main method, with KNN (K-Nearest Neighbors) and Decision Tree Regressor being the comparison methods. Testing results show that compared to KNN and Decision Tree, Random Forest Regressor yields the best performance through its steady results on every stage progression of all tested software projects
Artificial Intelligence Enabled Project Management: A Systematic Literature Review
In the Industry 5.0 era, companies are leveraging the potential of cutting-edge technologies such as artificial intelligence for more efficient and green human-centric production. In a similar approach, project management would benefit from artificial intelligence in order to achieve project goals by improving project performance, and consequently, reaching higher sustainable success. In this context, this paper examines the role of artificial intelligence in emerging project management through a systematic literature review; the applications of AI techniques in the project management performance domains are presented. The results show that the number of influential publications on artificial intelligence-enabled project management has increased significantly over the last decade. The findings indicate that artificial intelligence, predominantly machine learning, can be considerably useful in the management of construction and IT projects; it is notably encouraging for enhancing the planning, measurement, and uncertainty performance domains by providing promising forecasting and decision-making capabilities
- âŠ