The Experts below are selected from a list of 10158 Experts worldwide ranked by ideXlab platform
Margo Seltzer - One of the best experts on this subject based on the ideXlab platform.
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provbuild improving data scientist efficiency with provenance an extended abstract
International Conference on Software Engineering, 2020Co-Authors: Jiwon Joung, Maia Jacobs, Krzysztof Z Gajos, Margo SeltzerAbstract:Data scientists frequently analyze data by writing scripts. We conducted a contextual inquiry with interdisciplinary researchers, which revealed that parameter tuning is a highly iterative process and that Debugging is time-consuming. As analysis scripts evolve and become more complex, analysts have difficulty conceptualizing their workflow. In particular, after editing a script, it becomes difficult to determine precisely which code blocks depend on the edit. Consequently, scientists frequently re-run entire scripts instead of re-running only the necessary parts. We present ProvBuild, a data analysis environment that uses change impact analysis [1] to improve the iterative Debugging process in script-based workflow pipelines. ProvBuild is a Tool that leverages language-level provenance [2] to streamline the Debugging process by reducing programmer cognitive load and decreasing subsequent runtimes, leading to an overall reduction in elapsed Debugging time. ProvBuild uses provenance to track dependencies in a script. When an analyst debugs a script, ProvBuild generates a simplified script that contains only the information necessary to debug a particular problem. We demonstrate that Debugging the simplified script lowers a programmer’s cognitive load and permits faster re-execution when testing changes. The combination of reduced cognitive load and shorter runtime reduces the time necessary to debug a script. We quantitatively and qualitatively show that even though ProvBuild introduces overhead during a script’s first execution, it is a more efficient way for users to debug and tune complex workflows. ProvBuild demonstrates a novel use of language-level provenance, in which it is used to proactively improve programmer productively rather than merely providing a way to retroactively gain insight into a body of code. To the best of our knowledge, ProvBuild is a novel application of change impact analysis and it is the first Debugging Tool to leverage language-level provenance to reduce cognitive load and execution time.
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provbuild improving data scientist efficiency with provenance
International Conference on Software Engineering, 2020Co-Authors: Jiwon Joung, Maia Jacobs, Krzysztof Z Gajos, Margo SeltzerAbstract:Data scientists frequently analyze data by writing scripts. We conducted a contextual inquiry with interdisciplinary researchers, which revealed that parameter tuning is a highly iterative process and that Debugging is time-consuming. As analysis scripts evolve and become more complex, analysts have difficulty conceptualizing their workflow. In particular, after editing a script, it becomes difficult to determine precisely which code blocks depend on the edit. Consequently, scientists frequently re-run entire scripts instead of re-running only the necessary parts. We present ProvBuild, a data analysis environment that uses change impact analysis [1] to improve the iterative Debugging process in script-based workflow pipelines. ProvBuild is a Tool that leverages language-level provenance [2] to streamline the Debugging process by reducing programmer cognitive load and decreasing subsequent runtimes, leading to an overall reduction in elapsed Debugging time. ProvBuild uses provenance to track dependencies in a script. When an analyst debugs a script, ProvBuild generates a simplified script that contains only the information necessary to debug a particular problem. We demonstrate that Debugging the simplified script lowers a programmer's cognitive load and permits faster re-execution when testing changes. The combination of reduced cognitive load and shorter runtime reduces the time necessary to debug a script. We quantitatively and qualitatively show that even though ProvBuild introduces overhead during a script's first execution, it is a more efficient way for users to debug and tune complex workflows. ProvBuild demonstrates a novel use of language-level provenance, in which it is used to proactively improve programmer productively rather than merely providing a way to retroactively gain insight into a body of code. To the best of our knowledge, ProvBuild is a novel application of change impact analysis and it is the first Debugging Tool to leverage language-level provenance to reduce cognitive load and execution time.
Jiwon Joung - One of the best experts on this subject based on the ideXlab platform.
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provbuild improving data scientist efficiency with provenance an extended abstract
International Conference on Software Engineering, 2020Co-Authors: Jiwon Joung, Maia Jacobs, Krzysztof Z Gajos, Margo SeltzerAbstract:Data scientists frequently analyze data by writing scripts. We conducted a contextual inquiry with interdisciplinary researchers, which revealed that parameter tuning is a highly iterative process and that Debugging is time-consuming. As analysis scripts evolve and become more complex, analysts have difficulty conceptualizing their workflow. In particular, after editing a script, it becomes difficult to determine precisely which code blocks depend on the edit. Consequently, scientists frequently re-run entire scripts instead of re-running only the necessary parts. We present ProvBuild, a data analysis environment that uses change impact analysis [1] to improve the iterative Debugging process in script-based workflow pipelines. ProvBuild is a Tool that leverages language-level provenance [2] to streamline the Debugging process by reducing programmer cognitive load and decreasing subsequent runtimes, leading to an overall reduction in elapsed Debugging time. ProvBuild uses provenance to track dependencies in a script. When an analyst debugs a script, ProvBuild generates a simplified script that contains only the information necessary to debug a particular problem. We demonstrate that Debugging the simplified script lowers a programmer’s cognitive load and permits faster re-execution when testing changes. The combination of reduced cognitive load and shorter runtime reduces the time necessary to debug a script. We quantitatively and qualitatively show that even though ProvBuild introduces overhead during a script’s first execution, it is a more efficient way for users to debug and tune complex workflows. ProvBuild demonstrates a novel use of language-level provenance, in which it is used to proactively improve programmer productively rather than merely providing a way to retroactively gain insight into a body of code. To the best of our knowledge, ProvBuild is a novel application of change impact analysis and it is the first Debugging Tool to leverage language-level provenance to reduce cognitive load and execution time.
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provbuild improving data scientist efficiency with provenance
International Conference on Software Engineering, 2020Co-Authors: Jiwon Joung, Maia Jacobs, Krzysztof Z Gajos, Margo SeltzerAbstract:Data scientists frequently analyze data by writing scripts. We conducted a contextual inquiry with interdisciplinary researchers, which revealed that parameter tuning is a highly iterative process and that Debugging is time-consuming. As analysis scripts evolve and become more complex, analysts have difficulty conceptualizing their workflow. In particular, after editing a script, it becomes difficult to determine precisely which code blocks depend on the edit. Consequently, scientists frequently re-run entire scripts instead of re-running only the necessary parts. We present ProvBuild, a data analysis environment that uses change impact analysis [1] to improve the iterative Debugging process in script-based workflow pipelines. ProvBuild is a Tool that leverages language-level provenance [2] to streamline the Debugging process by reducing programmer cognitive load and decreasing subsequent runtimes, leading to an overall reduction in elapsed Debugging time. ProvBuild uses provenance to track dependencies in a script. When an analyst debugs a script, ProvBuild generates a simplified script that contains only the information necessary to debug a particular problem. We demonstrate that Debugging the simplified script lowers a programmer's cognitive load and permits faster re-execution when testing changes. The combination of reduced cognitive load and shorter runtime reduces the time necessary to debug a script. We quantitatively and qualitatively show that even though ProvBuild introduces overhead during a script's first execution, it is a more efficient way for users to debug and tune complex workflows. ProvBuild demonstrates a novel use of language-level provenance, in which it is used to proactively improve programmer productively rather than merely providing a way to retroactively gain insight into a body of code. To the best of our knowledge, ProvBuild is a novel application of change impact analysis and it is the first Debugging Tool to leverage language-level provenance to reduce cognitive load and execution time.
Krzysztof Z Gajos - One of the best experts on this subject based on the ideXlab platform.
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provbuild improving data scientist efficiency with provenance an extended abstract
International Conference on Software Engineering, 2020Co-Authors: Jiwon Joung, Maia Jacobs, Krzysztof Z Gajos, Margo SeltzerAbstract:Data scientists frequently analyze data by writing scripts. We conducted a contextual inquiry with interdisciplinary researchers, which revealed that parameter tuning is a highly iterative process and that Debugging is time-consuming. As analysis scripts evolve and become more complex, analysts have difficulty conceptualizing their workflow. In particular, after editing a script, it becomes difficult to determine precisely which code blocks depend on the edit. Consequently, scientists frequently re-run entire scripts instead of re-running only the necessary parts. We present ProvBuild, a data analysis environment that uses change impact analysis [1] to improve the iterative Debugging process in script-based workflow pipelines. ProvBuild is a Tool that leverages language-level provenance [2] to streamline the Debugging process by reducing programmer cognitive load and decreasing subsequent runtimes, leading to an overall reduction in elapsed Debugging time. ProvBuild uses provenance to track dependencies in a script. When an analyst debugs a script, ProvBuild generates a simplified script that contains only the information necessary to debug a particular problem. We demonstrate that Debugging the simplified script lowers a programmer’s cognitive load and permits faster re-execution when testing changes. The combination of reduced cognitive load and shorter runtime reduces the time necessary to debug a script. We quantitatively and qualitatively show that even though ProvBuild introduces overhead during a script’s first execution, it is a more efficient way for users to debug and tune complex workflows. ProvBuild demonstrates a novel use of language-level provenance, in which it is used to proactively improve programmer productively rather than merely providing a way to retroactively gain insight into a body of code. To the best of our knowledge, ProvBuild is a novel application of change impact analysis and it is the first Debugging Tool to leverage language-level provenance to reduce cognitive load and execution time.
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provbuild improving data scientist efficiency with provenance
International Conference on Software Engineering, 2020Co-Authors: Jiwon Joung, Maia Jacobs, Krzysztof Z Gajos, Margo SeltzerAbstract:Data scientists frequently analyze data by writing scripts. We conducted a contextual inquiry with interdisciplinary researchers, which revealed that parameter tuning is a highly iterative process and that Debugging is time-consuming. As analysis scripts evolve and become more complex, analysts have difficulty conceptualizing their workflow. In particular, after editing a script, it becomes difficult to determine precisely which code blocks depend on the edit. Consequently, scientists frequently re-run entire scripts instead of re-running only the necessary parts. We present ProvBuild, a data analysis environment that uses change impact analysis [1] to improve the iterative Debugging process in script-based workflow pipelines. ProvBuild is a Tool that leverages language-level provenance [2] to streamline the Debugging process by reducing programmer cognitive load and decreasing subsequent runtimes, leading to an overall reduction in elapsed Debugging time. ProvBuild uses provenance to track dependencies in a script. When an analyst debugs a script, ProvBuild generates a simplified script that contains only the information necessary to debug a particular problem. We demonstrate that Debugging the simplified script lowers a programmer's cognitive load and permits faster re-execution when testing changes. The combination of reduced cognitive load and shorter runtime reduces the time necessary to debug a script. We quantitatively and qualitatively show that even though ProvBuild introduces overhead during a script's first execution, it is a more efficient way for users to debug and tune complex workflows. ProvBuild demonstrates a novel use of language-level provenance, in which it is used to proactively improve programmer productively rather than merely providing a way to retroactively gain insight into a body of code. To the best of our knowledge, ProvBuild is a novel application of change impact analysis and it is the first Debugging Tool to leverage language-level provenance to reduce cognitive load and execution time.
Maia Jacobs - One of the best experts on this subject based on the ideXlab platform.
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provbuild improving data scientist efficiency with provenance an extended abstract
International Conference on Software Engineering, 2020Co-Authors: Jiwon Joung, Maia Jacobs, Krzysztof Z Gajos, Margo SeltzerAbstract:Data scientists frequently analyze data by writing scripts. We conducted a contextual inquiry with interdisciplinary researchers, which revealed that parameter tuning is a highly iterative process and that Debugging is time-consuming. As analysis scripts evolve and become more complex, analysts have difficulty conceptualizing their workflow. In particular, after editing a script, it becomes difficult to determine precisely which code blocks depend on the edit. Consequently, scientists frequently re-run entire scripts instead of re-running only the necessary parts. We present ProvBuild, a data analysis environment that uses change impact analysis [1] to improve the iterative Debugging process in script-based workflow pipelines. ProvBuild is a Tool that leverages language-level provenance [2] to streamline the Debugging process by reducing programmer cognitive load and decreasing subsequent runtimes, leading to an overall reduction in elapsed Debugging time. ProvBuild uses provenance to track dependencies in a script. When an analyst debugs a script, ProvBuild generates a simplified script that contains only the information necessary to debug a particular problem. We demonstrate that Debugging the simplified script lowers a programmer’s cognitive load and permits faster re-execution when testing changes. The combination of reduced cognitive load and shorter runtime reduces the time necessary to debug a script. We quantitatively and qualitatively show that even though ProvBuild introduces overhead during a script’s first execution, it is a more efficient way for users to debug and tune complex workflows. ProvBuild demonstrates a novel use of language-level provenance, in which it is used to proactively improve programmer productively rather than merely providing a way to retroactively gain insight into a body of code. To the best of our knowledge, ProvBuild is a novel application of change impact analysis and it is the first Debugging Tool to leverage language-level provenance to reduce cognitive load and execution time.
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provbuild improving data scientist efficiency with provenance
International Conference on Software Engineering, 2020Co-Authors: Jiwon Joung, Maia Jacobs, Krzysztof Z Gajos, Margo SeltzerAbstract:Data scientists frequently analyze data by writing scripts. We conducted a contextual inquiry with interdisciplinary researchers, which revealed that parameter tuning is a highly iterative process and that Debugging is time-consuming. As analysis scripts evolve and become more complex, analysts have difficulty conceptualizing their workflow. In particular, after editing a script, it becomes difficult to determine precisely which code blocks depend on the edit. Consequently, scientists frequently re-run entire scripts instead of re-running only the necessary parts. We present ProvBuild, a data analysis environment that uses change impact analysis [1] to improve the iterative Debugging process in script-based workflow pipelines. ProvBuild is a Tool that leverages language-level provenance [2] to streamline the Debugging process by reducing programmer cognitive load and decreasing subsequent runtimes, leading to an overall reduction in elapsed Debugging time. ProvBuild uses provenance to track dependencies in a script. When an analyst debugs a script, ProvBuild generates a simplified script that contains only the information necessary to debug a particular problem. We demonstrate that Debugging the simplified script lowers a programmer's cognitive load and permits faster re-execution when testing changes. The combination of reduced cognitive load and shorter runtime reduces the time necessary to debug a script. We quantitatively and qualitatively show that even though ProvBuild introduces overhead during a script's first execution, it is a more efficient way for users to debug and tune complex workflows. ProvBuild demonstrates a novel use of language-level provenance, in which it is used to proactively improve programmer productively rather than merely providing a way to retroactively gain insight into a body of code. To the best of our knowledge, ProvBuild is a novel application of change impact analysis and it is the first Debugging Tool to leverage language-level provenance to reduce cognitive load and execution time.
Rok Sosic - One of the best experts on this subject based on the ideXlab platform.
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a Debugging Tool for software evolution
Working Conference on Reverse Engineering, 1995Co-Authors: David Abramson, Rok SosicAbstract:This paper describes a Tool for Debugging programs which have been developed using evolutionary software techniques. The Tool enhances the traditional Debugging approach by automating the comparison of data structures between two running programs. Using this technique, it is possible to use early versions of a program which are known to operate correctly to generate values for comparison with the new program under development. The Tool allows the reference code and the program being developed to execute on different computer systems by using open distributed systems techniques. A simple visualisation system allows the user to view the differences in data structures. By using the data flow of the code, it is possible to locate faulty sections of code rapidly. A small case study of finding an error in a scientific code is given.
