The Experts below are selected from a list of 276 Experts worldwide ranked by ideXlab platform
Percy Liang - One of the best experts on this subject based on the ideXlab platform.
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spoc search based Pseudocode to code
Neural Information Processing Systems, 2019Co-Authors: Sumith Kulal, Panupong Pasupat, Kartik Chandra, Oded Padon, Alex Aiken, Percy LiangAbstract:We consider the task of mapping Pseudocode to executable code, assuming a one-to-one correspondence between lines of Pseudocode and lines of code. Given test cases as a mechanism to validate programs, we search over the space of possible translations of the Pseudocode to find a program that compiles and passes the test cases. While performing a best-first search, compilation errors constitute 88.7% of program failures. To better guide this search, we learn to predict the line of the program responsible for the failure and focus search over alternative translations of the Pseudocode for that line. For evaluation, we collected the SPoC dataset (Search-based Pseudocode to Code) containing 18,356 C++ programs with human-authored Pseudocode and test cases. Under a budget of 100 program compilations, performing search improves the synthesis success rate over using the top-one translation of the Pseudocode from 25.6% to 44.7%.
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NeurIPS - SPoC: Search-based Pseudocode to Code
2019Co-Authors: Sumith Kulal, Panupong Pasupat, Kartik Chandra, Oded Padon, Alex Aiken, Percy LiangAbstract:We consider the task of mapping Pseudocode to executable code, assuming a one-to-one correspondence between lines of Pseudocode and lines of code. Given test cases as a mechanism to validate programs, we search over the space of possible translations of the Pseudocode to find a program that compiles and passes the test cases. While performing a best-first search, compilation errors constitute 88.7% of program failures. To better guide this search, we learn to predict the line of the program responsible for the failure and focus search over alternative translations of the Pseudocode for that line. For evaluation, we collected the SPoC dataset (Search-based Pseudocode to Code) containing 18,356 C++ programs with human-authored Pseudocode and test cases. Under a budget of 100 program compilations, performing search improves the synthesis success rate over using the top-one translation of the Pseudocode from 25.6% to 44.7%.
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spoc search based Pseudocode to code
arXiv: Learning, 2019Co-Authors: Sumith Kulal, Panupong Pasupat, Kartik Chandra, Oded Padon, Alex Aiken, Percy LiangAbstract:We consider the task of mapping Pseudocode to long programs that are functionally correct. Given test cases as a mechanism to validate programs, we search over the space of possible translations of the Pseudocode to find a program that passes the validation. However, without proper credit assignment to localize the sources of program failures, it is difficult to guide search toward more promising programs. We propose to perform credit assignment based on signals from compilation errors, which constitute 88.7% of program failures. Concretely, we treat the translation of each Pseudocode line as a discrete portion of the program, and whenever a synthesized program fails to compile, an error localization method tries to identify the portion of the program responsible for the failure. We then focus search over alternative translations of the Pseudocode for those portions. For evaluation, we collected the SPoC dataset (Search-based Pseudocode to Code) containing 18,356 programs with human-authored Pseudocode and test cases. Under a budget of 100 program compilations, performing search improves the synthesis success rate over using the top-one translation of the Pseudocode from 25.6% to 44.7%.
Sumith Kulal - One of the best experts on this subject based on the ideXlab platform.
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spoc search based Pseudocode to code
Neural Information Processing Systems, 2019Co-Authors: Sumith Kulal, Panupong Pasupat, Kartik Chandra, Oded Padon, Alex Aiken, Percy LiangAbstract:We consider the task of mapping Pseudocode to executable code, assuming a one-to-one correspondence between lines of Pseudocode and lines of code. Given test cases as a mechanism to validate programs, we search over the space of possible translations of the Pseudocode to find a program that compiles and passes the test cases. While performing a best-first search, compilation errors constitute 88.7% of program failures. To better guide this search, we learn to predict the line of the program responsible for the failure and focus search over alternative translations of the Pseudocode for that line. For evaluation, we collected the SPoC dataset (Search-based Pseudocode to Code) containing 18,356 C++ programs with human-authored Pseudocode and test cases. Under a budget of 100 program compilations, performing search improves the synthesis success rate over using the top-one translation of the Pseudocode from 25.6% to 44.7%.
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NeurIPS - SPoC: Search-based Pseudocode to Code
2019Co-Authors: Sumith Kulal, Panupong Pasupat, Kartik Chandra, Oded Padon, Alex Aiken, Percy LiangAbstract:We consider the task of mapping Pseudocode to executable code, assuming a one-to-one correspondence between lines of Pseudocode and lines of code. Given test cases as a mechanism to validate programs, we search over the space of possible translations of the Pseudocode to find a program that compiles and passes the test cases. While performing a best-first search, compilation errors constitute 88.7% of program failures. To better guide this search, we learn to predict the line of the program responsible for the failure and focus search over alternative translations of the Pseudocode for that line. For evaluation, we collected the SPoC dataset (Search-based Pseudocode to Code) containing 18,356 C++ programs with human-authored Pseudocode and test cases. Under a budget of 100 program compilations, performing search improves the synthesis success rate over using the top-one translation of the Pseudocode from 25.6% to 44.7%.
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spoc search based Pseudocode to code
arXiv: Learning, 2019Co-Authors: Sumith Kulal, Panupong Pasupat, Kartik Chandra, Oded Padon, Alex Aiken, Percy LiangAbstract:We consider the task of mapping Pseudocode to long programs that are functionally correct. Given test cases as a mechanism to validate programs, we search over the space of possible translations of the Pseudocode to find a program that passes the validation. However, without proper credit assignment to localize the sources of program failures, it is difficult to guide search toward more promising programs. We propose to perform credit assignment based on signals from compilation errors, which constitute 88.7% of program failures. Concretely, we treat the translation of each Pseudocode line as a discrete portion of the program, and whenever a synthesized program fails to compile, an error localization method tries to identify the portion of the program responsible for the failure. We then focus search over alternative translations of the Pseudocode for those portions. For evaluation, we collected the SPoC dataset (Search-based Pseudocode to Code) containing 18,356 programs with human-authored Pseudocode and test cases. Under a budget of 100 program compilations, performing search improves the synthesis success rate over using the top-one translation of the Pseudocode from 25.6% to 44.7%.
Alex Aiken - One of the best experts on this subject based on the ideXlab platform.
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spoc search based Pseudocode to code
Neural Information Processing Systems, 2019Co-Authors: Sumith Kulal, Panupong Pasupat, Kartik Chandra, Oded Padon, Alex Aiken, Percy LiangAbstract:We consider the task of mapping Pseudocode to executable code, assuming a one-to-one correspondence between lines of Pseudocode and lines of code. Given test cases as a mechanism to validate programs, we search over the space of possible translations of the Pseudocode to find a program that compiles and passes the test cases. While performing a best-first search, compilation errors constitute 88.7% of program failures. To better guide this search, we learn to predict the line of the program responsible for the failure and focus search over alternative translations of the Pseudocode for that line. For evaluation, we collected the SPoC dataset (Search-based Pseudocode to Code) containing 18,356 C++ programs with human-authored Pseudocode and test cases. Under a budget of 100 program compilations, performing search improves the synthesis success rate over using the top-one translation of the Pseudocode from 25.6% to 44.7%.
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NeurIPS - SPoC: Search-based Pseudocode to Code
2019Co-Authors: Sumith Kulal, Panupong Pasupat, Kartik Chandra, Oded Padon, Alex Aiken, Percy LiangAbstract:We consider the task of mapping Pseudocode to executable code, assuming a one-to-one correspondence between lines of Pseudocode and lines of code. Given test cases as a mechanism to validate programs, we search over the space of possible translations of the Pseudocode to find a program that compiles and passes the test cases. While performing a best-first search, compilation errors constitute 88.7% of program failures. To better guide this search, we learn to predict the line of the program responsible for the failure and focus search over alternative translations of the Pseudocode for that line. For evaluation, we collected the SPoC dataset (Search-based Pseudocode to Code) containing 18,356 C++ programs with human-authored Pseudocode and test cases. Under a budget of 100 program compilations, performing search improves the synthesis success rate over using the top-one translation of the Pseudocode from 25.6% to 44.7%.
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spoc search based Pseudocode to code
arXiv: Learning, 2019Co-Authors: Sumith Kulal, Panupong Pasupat, Kartik Chandra, Oded Padon, Alex Aiken, Percy LiangAbstract:We consider the task of mapping Pseudocode to long programs that are functionally correct. Given test cases as a mechanism to validate programs, we search over the space of possible translations of the Pseudocode to find a program that passes the validation. However, without proper credit assignment to localize the sources of program failures, it is difficult to guide search toward more promising programs. We propose to perform credit assignment based on signals from compilation errors, which constitute 88.7% of program failures. Concretely, we treat the translation of each Pseudocode line as a discrete portion of the program, and whenever a synthesized program fails to compile, an error localization method tries to identify the portion of the program responsible for the failure. We then focus search over alternative translations of the Pseudocode for those portions. For evaluation, we collected the SPoC dataset (Search-based Pseudocode to Code) containing 18,356 programs with human-authored Pseudocode and test cases. Under a budget of 100 program compilations, performing search improves the synthesis success rate over using the top-one translation of the Pseudocode from 25.6% to 44.7%.
Oded Padon - One of the best experts on this subject based on the ideXlab platform.
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spoc search based Pseudocode to code
Neural Information Processing Systems, 2019Co-Authors: Sumith Kulal, Panupong Pasupat, Kartik Chandra, Oded Padon, Alex Aiken, Percy LiangAbstract:We consider the task of mapping Pseudocode to executable code, assuming a one-to-one correspondence between lines of Pseudocode and lines of code. Given test cases as a mechanism to validate programs, we search over the space of possible translations of the Pseudocode to find a program that compiles and passes the test cases. While performing a best-first search, compilation errors constitute 88.7% of program failures. To better guide this search, we learn to predict the line of the program responsible for the failure and focus search over alternative translations of the Pseudocode for that line. For evaluation, we collected the SPoC dataset (Search-based Pseudocode to Code) containing 18,356 C++ programs with human-authored Pseudocode and test cases. Under a budget of 100 program compilations, performing search improves the synthesis success rate over using the top-one translation of the Pseudocode from 25.6% to 44.7%.
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NeurIPS - SPoC: Search-based Pseudocode to Code
2019Co-Authors: Sumith Kulal, Panupong Pasupat, Kartik Chandra, Oded Padon, Alex Aiken, Percy LiangAbstract:We consider the task of mapping Pseudocode to executable code, assuming a one-to-one correspondence between lines of Pseudocode and lines of code. Given test cases as a mechanism to validate programs, we search over the space of possible translations of the Pseudocode to find a program that compiles and passes the test cases. While performing a best-first search, compilation errors constitute 88.7% of program failures. To better guide this search, we learn to predict the line of the program responsible for the failure and focus search over alternative translations of the Pseudocode for that line. For evaluation, we collected the SPoC dataset (Search-based Pseudocode to Code) containing 18,356 C++ programs with human-authored Pseudocode and test cases. Under a budget of 100 program compilations, performing search improves the synthesis success rate over using the top-one translation of the Pseudocode from 25.6% to 44.7%.
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spoc search based Pseudocode to code
arXiv: Learning, 2019Co-Authors: Sumith Kulal, Panupong Pasupat, Kartik Chandra, Oded Padon, Alex Aiken, Percy LiangAbstract:We consider the task of mapping Pseudocode to long programs that are functionally correct. Given test cases as a mechanism to validate programs, we search over the space of possible translations of the Pseudocode to find a program that passes the validation. However, without proper credit assignment to localize the sources of program failures, it is difficult to guide search toward more promising programs. We propose to perform credit assignment based on signals from compilation errors, which constitute 88.7% of program failures. Concretely, we treat the translation of each Pseudocode line as a discrete portion of the program, and whenever a synthesized program fails to compile, an error localization method tries to identify the portion of the program responsible for the failure. We then focus search over alternative translations of the Pseudocode for those portions. For evaluation, we collected the SPoC dataset (Search-based Pseudocode to Code) containing 18,356 programs with human-authored Pseudocode and test cases. Under a budget of 100 program compilations, performing search improves the synthesis success rate over using the top-one translation of the Pseudocode from 25.6% to 44.7%.
Kartik Chandra - One of the best experts on this subject based on the ideXlab platform.
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spoc search based Pseudocode to code
Neural Information Processing Systems, 2019Co-Authors: Sumith Kulal, Panupong Pasupat, Kartik Chandra, Oded Padon, Alex Aiken, Percy LiangAbstract:We consider the task of mapping Pseudocode to executable code, assuming a one-to-one correspondence between lines of Pseudocode and lines of code. Given test cases as a mechanism to validate programs, we search over the space of possible translations of the Pseudocode to find a program that compiles and passes the test cases. While performing a best-first search, compilation errors constitute 88.7% of program failures. To better guide this search, we learn to predict the line of the program responsible for the failure and focus search over alternative translations of the Pseudocode for that line. For evaluation, we collected the SPoC dataset (Search-based Pseudocode to Code) containing 18,356 C++ programs with human-authored Pseudocode and test cases. Under a budget of 100 program compilations, performing search improves the synthesis success rate over using the top-one translation of the Pseudocode from 25.6% to 44.7%.
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NeurIPS - SPoC: Search-based Pseudocode to Code
2019Co-Authors: Sumith Kulal, Panupong Pasupat, Kartik Chandra, Oded Padon, Alex Aiken, Percy LiangAbstract:We consider the task of mapping Pseudocode to executable code, assuming a one-to-one correspondence between lines of Pseudocode and lines of code. Given test cases as a mechanism to validate programs, we search over the space of possible translations of the Pseudocode to find a program that compiles and passes the test cases. While performing a best-first search, compilation errors constitute 88.7% of program failures. To better guide this search, we learn to predict the line of the program responsible for the failure and focus search over alternative translations of the Pseudocode for that line. For evaluation, we collected the SPoC dataset (Search-based Pseudocode to Code) containing 18,356 C++ programs with human-authored Pseudocode and test cases. Under a budget of 100 program compilations, performing search improves the synthesis success rate over using the top-one translation of the Pseudocode from 25.6% to 44.7%.
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spoc search based Pseudocode to code
arXiv: Learning, 2019Co-Authors: Sumith Kulal, Panupong Pasupat, Kartik Chandra, Oded Padon, Alex Aiken, Percy LiangAbstract:We consider the task of mapping Pseudocode to long programs that are functionally correct. Given test cases as a mechanism to validate programs, we search over the space of possible translations of the Pseudocode to find a program that passes the validation. However, without proper credit assignment to localize the sources of program failures, it is difficult to guide search toward more promising programs. We propose to perform credit assignment based on signals from compilation errors, which constitute 88.7% of program failures. Concretely, we treat the translation of each Pseudocode line as a discrete portion of the program, and whenever a synthesized program fails to compile, an error localization method tries to identify the portion of the program responsible for the failure. We then focus search over alternative translations of the Pseudocode for those portions. For evaluation, we collected the SPoC dataset (Search-based Pseudocode to Code) containing 18,356 programs with human-authored Pseudocode and test cases. Under a budget of 100 program compilations, performing search improves the synthesis success rate over using the top-one translation of the Pseudocode from 25.6% to 44.7%.
