Workshop Co-chairs Hira Agrawal, Telcordia Technologies, USA Zhenyu Chen, Nanjing University, Chi... more Workshop Co-chairs Hira Agrawal, Telcordia Technologies, USA Zhenyu Chen, Nanjing University, China ... Steering Committee Co-chairs W. Eric Wong , University of Texas at Dallas, USA TH Tse , The University of Hong Kong, Hong Kong ... Steering Committee James Jones, University of California, Irvine, USA Franz Wotawa, Graz University of Technology, Austria WK Chan, City University of Hong Kong, Hong Kong Hira Agrawal, Telcordia Technologies, USA ... Program Committee Rui Abreu, University of Porto, Portugal Mikhail Auguston, Naval ...
Data dependences are building blocks for techniques such as program slicing and, thus, are useful... more Data dependences are building blocks for techniques such as program slicing and, thus, are useful for program comprehension and debugging. Unfortunately, static analysis identifies many data dependences that are infeasible or unlikely to occur at runtime. Dynamic analysis, in contrast, does not identify all data dependences that can occur in a program.
The constant modification of software during its life cycle poses many challenges for developers ... more The constant modification of software during its life cycle poses many challenges for developers and testers because changes might not behave as expected or may introduce erroneous side effects. For those reasons, it is of critical importance to analyze, test, and validate software every time it changes. The most common method for validating modified software is regression testing, which identifies differences in the behavior of software caused by changes and determines the correctness of those differences.
Abstract—Program slicing is a popular but imprecise technique for identifying which parts of a pr... more Abstract—Program slicing is a popular but imprecise technique for identifying which parts of a program affect or are affected by a particular value. A major reason for this imprecision is that slicing reports all program statements that may belong to a slice, regardless of how relevant to the target value they are. To address this problem, we introduce quantitative slicing (q-slicing), a novel approach that quantifies the relevance of each statement in a slice.
Workshop Co-chairs Hira Agrawal, Telcordia Technologies, USA Zhenyu Chen, Nanjing University, Chi... more Workshop Co-chairs Hira Agrawal, Telcordia Technologies, USA Zhenyu Chen, Nanjing University, China ... Steering Committee Co-chairs W. Eric Wong , University of Texas at Dallas, USA TH Tse , The University of Hong Kong, Hong Kong ... Steering Committee James Jones, University of California, Irvine, USA Franz Wotawa, Graz University of Technology, Austria WK Chan, City University of Hong Kong, Hong Kong Hira Agrawal, Telcordia Technologies, USA ... Program Committee Rui Abreu, University of Porto, Portugal Mikhail Auguston, Naval ...
Data dependences are building blocks for techniques such as program slicing and, thus, are useful... more Data dependences are building blocks for techniques such as program slicing and, thus, are useful for program comprehension and debugging. Unfortunately, static analysis identifies many data dependences that are infeasible or unlikely to occur at runtime. Dynamic analysis, in contrast, does not identify all data dependences that can occur in a program.
The constant modification of software during its life cycle poses many challenges for developers ... more The constant modification of software during its life cycle poses many challenges for developers and testers because changes might not behave as expected or may introduce erroneous side effects. For those reasons, it is of critical importance to analyze, test, and validate software every time it changes. The most common method for validating modified software is regression testing, which identifies differences in the behavior of software caused by changes and determines the correctness of those differences.
Abstract—Program slicing is a popular but imprecise technique for identifying which parts of a pr... more Abstract—Program slicing is a popular but imprecise technique for identifying which parts of a program affect or are affected by a particular value. A major reason for this imprecision is that slicing reports all program statements that may belong to a slice, regardless of how relevant to the target value they are. To address this problem, we introduce quantitative slicing (q-slicing), a novel approach that quantifies the relevance of each statement in a slice.
Effectively incorporating technology into the classroom is a great challenge faced by schools tod... more Effectively incorporating technology into the classroom is a great challenge faced by schools today. In this article, we propose a Mobile Computer Supported Collaborative Learning (MCSCL) system to support high school teachers with wirelessly networked Handheld Computers. This system promotes student collaboration and constructivism, without losing face-to-face contact.
The MCSCL system was tested during a five week experience in a high school physics class. We observed both its qualitative and quantitative impact. Students and teachers responded very favorably to the system, and the experience also had a strong social impact outside the classroom. The MCSCL system provided a highly motivating learning environment that changed classroom dynamics and promoted collaboration between students. We obtained statistically significant results showing that the environment created by combining the teacher's instruction with the MSCSL system enabled the students to construct new knowledge based upon the previous knowledge provided by the teacher.
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The MCSCL system was tested during a five week experience in a high school physics class. We observed both its qualitative and quantitative impact. Students and teachers responded very favorably to the system, and the experience also had a strong social impact outside the classroom. The MCSCL system provided a highly motivating learning environment that changed classroom dynamics and promoted collaboration between students. We obtained statistically significant results showing that the environment created by combining the teacher's instruction with the MSCSL system enabled the students to construct new knowledge based upon the previous knowledge provided by the teacher.