Algorithmic Program Debugging
Abstract
Productively combines elements of programming languages, environments, logic, and inductive inference to produce effective debugging aids. Its use of the PROLOG language provides an efficient implementation of the debugging algorithms.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Refinement operators for clauses and theories have been studied in detail in ILP.
“By contrast, refinement operators for clauses (Shapiro 1983; De Raedt and Bruynooghe 1993; Nienhuys-Cheng and de Wolf 1997) and theories (Nienhuys-Cheng and de Wolf 1997; Midelfart 1999; Badea 2001) have been studied in detail in ILP.”
From Learning programs by learning from failures (2021) · cites this paper as Shapiro 1983
ILP is a form of program synthesis, where the goal is to automatically build executable programs.
“This scenario shows that ILP is a form of program synthesis (Shapiro, 1983), where the goal is to automatically build executable programs.”
From Inductive Logic Programming At 30: A New Introduction (2022) · cites this paper as Shapiro, 1983
Shapiro's (1983) work on inducing Prolog programs made major contributions to ILP, including the concepts of backtracking and refinement operators.
“Shapiro's (1983) work on inducing Prolog programs made major contributions to ILP, including the concepts of backtracking and refinement operators.”
From Inductive Logic Programming At 30: A New Introduction (2022) · cites this paper as Shapiro, 1983
ILP systems that focus on program synthesis tend to induce definite programs, typically as Prolog programs.
“Systems that focus on program synthesis (Shapiro, 1983; Bratko, 1999; Ahlgren & Yuen, 2013; Cropper & Muggleton, 2016, 2019; Cropper & Morel, 2021) tend to induce definite programs, typically as Prolog programs.”
From Inductive Logic Programming At 30: A New Introduction (2022) · cites this paper as Shapiro, 1983
LFE is by far the most popular ILP setting.
“LFE is by far the most popular ILP setting (Shapiro, 1983; Muggleton, 1987; Muggleton & Buntine, 1988; Muggleton & Feng, 1990; Quinlan, 1990; Muggleton, 1995; Bratko, 1999; Srinivasan, 2001; Ray, 2009; Ahlgren & Yuen, 2013; Muggleton et al., 2015; Cropper & Muggleton, 2016; Kaminski et al., 2018; Cropper & Morel, 2021).”
From Inductive Logic Programming At 30: A New Introduction (2022) · cites this paper as Shapiro, 1983
Some systems induce theories formed of multiple clauses and thus require generality orders over clausal theories.
“However, some systems (Shapiro, 1983; Bratko, 1999; Cropper & Morel, 2021) induce theories formed of multiple clauses and thus require generality orders over clausal theories .”
From Inductive Logic Programming At 30: A New Introduction (2022) · cites this paper as Shapiro, 1983
Because ILP induces programs, it is also a form of program synthesis.
“Because ILP induces programs, it is also a form of program synthesis (Shapiro, 1983), where the goal is to build a program from a specification.”
From Inductive Logic Programming At 30: A New Introduction (2022) · cites this paper as Shapiro, 1983
Cited by
Sorted by citations
Learning Logical Definitions from Relations
Quinlan, J. R. (1990) · Machine Learning · doi:10.1023/a:1022699322624 · Cited by 1,644 · see citing works
A Survey on Software Fault Localization
Wong, W. E., Gao, R., Li, Y., Abreu, R., & Wotawa, F. (2016) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2016.2521368 · Cited by 1,110 · see citing works
Alur, R., Bodik, R., Juniwal, G., Martin, M. M. K., Raghothaman, M., Seshia, S. A., Singh, R., Solar-Lezama, A., Torlak, E., & Udupa, A. (2013) · 2013 Formal Methods in Computer-Aided Design · doi:10.1109/fmcad.2013.6679385 · Cited by 656 · see citing works
Learning programs by learning from failures
Cropper, A., & Morel, R. (2021) · Machine Learning · doi:10.1007/s10994-020-05934-z · Cited by 86 · see citing works
“…1990;Muggleton 1995; De Raedt and Bruynooghe 1993; Blockeel and De Raedt 1998; Srinivasan 2001; Ahlgren and Yuen 2013) or refine a hypothesis (Shapiro 1983; Bratko 1999; Athakravi et al. 2013; Cropper and Muggleton 2016), our approach refines the hypothesis space through learned hypothesis…”
BackgroundMethodInfluentialInductive Logic Programming At 30: A New Introduction
Cropper, A., & Dumančić, S. (2022) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.13507 · Cited by 83 · see citing works
“ILP systems that aim to induce algorithms and general-purpose programs (Shapiro, 1983; Cropper & Muggleton, 2019; Cropper & Morel, 2020) often induce definite programs, typically as Prolog programs.”
BackgroundInfluentialLogical reduction of metarules
Cropper, A., & Tourret, S. (2020) · Machine Learning · doi:10.1007/s10994-019-05834-x · Cited by 29 · see citing works
- Authors
- Shapiro, E. Y.
- Corresponding author
- Shapiro, Ehud Y.
- ORCID
- Shapiro, Ehud Y.
- Venue
- The MIT Press eBooks
- Publisher
- The MIT Press
- Published
- 1983-04-14
- OpenAlex
- W1514468887
- Cited by
- 1,085
- References
- 0
- Citations by year
- 2026: 6 · 2025: 6 · 2024: 3 · 2023: 14 · 2022: 8 · 2021: 12 · 2020: 13 · 2019: 14 · 2018: 16 · 2017: 16 · 2016: 21 · 2015: 14 · 2014: 18 · 2013: 30 · 2012: 34
- FWCI
- 31.35
- Type
- book
- Language
- en
- Open access
- closed
- Locations
- The MIT Press eBooks (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Software › Software Testing and Debugging Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Logic, programming, and type systems
- Physical Sciences › Computer Science › Computer Science Applications › Teaching and Learning Programming
- Keywords
- Prolog, inductive reasoning, logic programming, program analysis
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
