A Survey of Algorithmic Debugging

"© ACM, 2017. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in ACM Computing Surveys, {50, 4, 2017} https://dl.acm.org/doi/10.1145/3106740"[EN] Algorithmic debugging is a technique proposed in 1982 by E. Y. Shapiro in the context of logic programming. This survey shows how the initial ideas have been developed to become a widespread debugging schema ftting many diferent programming paradigms and with applications out of the program debugging feld. We describe the general framework and the main issues related to the implementations in diferent programming paradigms and discuss several proposed improvements and optimizations. We also review the main algorithmic debugger tools that have been implemented so far and compare their features. From this comparison, we elaborate a summary of desirable characteristics that should be considered when implementing future algorithmic debuggers.This work has been partially supported by the EU (FEDER) and the Spanish Ministerio de Economia y Competitividad under grant TIN2013-44742-C4-1-R, TIN2016-76843-C4-1-R, StrongSoft (TIN2012-39391-C04-04), and TRACES (TIN2015-67522-C3-3-R) by the Generalitat Valenciana under grant PROMETEO-II/2015/013 (SmartLogic) and by the Comunidad de Madrid project N-Greens Software-CM (S2013/ICE-2731).Caballero, R.; Riesco, A.; Silva, J. (2017). A Survey of Algorithmic Debugging. ACM Computing Surveys. 50(4):1-35. https://doi.org/10.1145/3106740S135504Abramson, D., Foster, I., Michalakes, J., & Sosič, R. (1996). Relative debugging. Communications of the ACM, 39(11), 69-77. doi:10.1145/240455.240475K. R. Apt H. A. Blair and A. Walker. 1988. Towards a theory of declarative knowledge. In Foundations of Deductive Databases and Logic Programming J. Minker (Ed.). Morgan Kaufmann Publishers Inc. San Francisco CA 89--148. 10.1016/B978-0-934613-40-8.50006-3 K. R. Apt H. A. Blair and A. Walker. 1988. Towards a theory of declarative knowledge. In Foundations of Deductive Databases and Logic Programming J. Minker (Ed.). Morgan Kaufmann Publishers Inc. San Francisco CA 89--148. 10.1016/B978-0-934613-40-8.50006-3Arora, T., Ramakrishnan, R., Roth, W. G., Seshadri, P., & Srivastava, D. (1993). Explaining program execution in deductive systems. Lecture Notes in Computer Science, 101-119. doi:10.1007/3-540-57530-8_7E. Av-Ron. 1984. Top-Down Diagnosis of Prolog Programs. Ph.D. Dissertation. Weizmann Institute. E. Av-Ron. 1984. Top-Down Diagnosis of Prolog Programs. Ph.D. Dissertation. Weizmann Institute.A. Beaulieu. 2005. Learning SQL. O’Reilly Farnham UK. A. Beaulieu. 2005. Learning SQL. O’Reilly Farnham UK.D. Binks. 1995. Declarative Debugging in Gödel. Ph.D. Dissertation. University of Bristol. D. Binks. 1995. Declarative Debugging in Gödel. Ph.D. Dissertation. University of Bristol.B. Braßel and H. Siegel. 2008. Debugging Lazy Functional Programs by Asking the Oracle. Springer-Verlag Berlin 183--200. DOI:http://dx.doi.org/10.1007/978-3-540-85373-2_11 10.1007/978-3-540-85373-2_11 B. Braßel and H. Siegel. 2008. Debugging Lazy Functional Programs by Asking the Oracle. Springer-Verlag Berlin 183--200. DOI:http://dx.doi.org/10.1007/978-3-540-85373-2_11 10.1007/978-3-540-85373-2_11Caballero, R. (2005). A declarative debugger of incorrect answers for constraint functional-logic programs. Proceedings of the 2005 ACM SIGPLAN workshop on Curry and functional logic programming - WCFLP ’05. doi:10.1145/1085099.1085102Caballero, R., García-Ruiz, Y., & Sáenz-Pérez, F. (2012). Declarative Debugging of Wrong and Missing Answers for SQL Views. Lecture Notes in Computer Science, 73-87. doi:10.1007/978-3-642-29822-6_9Caballero, R., García-Ruiz, Y., & Sáenz-Pérez, F. (2015). 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Termination Proofs for Logic Programs with Tabling

Tabled logic programming is receiving increasing attention in the Logic Programming community. It avoids many of the shortcomings of SLD execution and provides a more flexible and often extremely efficient execution mechanism for logic programs. In particular, tabled execution of logic programs terminates more often than execution based on SLD-resolution. In this article, we introduce two notions of universal termination of logic programming with Tabling: quasi-termination and (the stronger notion of) LG-termination. We present sufficient conditions for these two notions of termination, namely quasi-acceptability and LG-acceptability, and we show that these conditions are also necessary in case the tabling is well-chosen. Starting from these conditions, we give modular termination proofs, i.e., proofs capable of combining termination proofs of separate programs to obtain termination proofs of combined programs. Finally, in the presence of mode information, we state sufficient conditions which form the basis for automatically proving termination in a constraint-based way.Comment: 48 pages, 6 figures, submitted to ACM Transactions on Computational Logic (TOCL

On Language Processors and Software Maintenance

This work investigates declarative transformation tools in the context of software maintenance. Besides maintenance of the language specification, evolution of a software language requires the adaptation of the software written in that language as well as the adaptation of the software that transforms software written in the evolving language. This co-evolution is studied to derive automatic adaptations of artefacts from adaptations of the language specification. Furthermore, AOP for Prolog is introduced to improve maintainability of language specifications and derived tools.Die Arbeit unterstützt deklarative Transformationswerkzeuge im Kontext der Softwarewartung. Neben der Wartung der Sprachbeschreibung erfordert die Evolution einer Sprache sowohl die Anpassung der Software, die in dieser Sprache geschrieben ist als auch die Anpassung der Software, die diese Software transformiert. Diese Koevolution wird untersucht, um automatische Anpassungen von Artefakten von Anpassungen der Sprachbeschreibungen abzuleiten. Weiterhin wird AOP für Prolog eingeführt, um die Wartbarkeit von Sprachbeschreibungen und den daraus abgeleiteten Werkzeugen zu erhöhen

Poly-controlled partial evaluation and its application to resource-aware program specialization

La Evaluación Parcial es una técnica automática para la optimización de programas. Su objetivo principal es el de especializar un programa con respecto a parte de sus datos de entrada, los que se conocen como datos estáticos. La calidad del código generado por la evaluación parcial de programas lógicos depende, en gran medida, de la estrategia de control que se haya empleado. Desafortunadamente, aún estamos lejos de contar con una estrategia de control suficientemente sofisticada como para comportarse de manera óptima para cualquier programa. La principal contribución de esta tesis es el desarrollo de la Evaluación Parcial Policontrolada, un novedoso entorno para la evaluación parcial de programas lógicos, el cual es policontrolado en el sentido de que puede tomar en cuenta conjuntos de reglas de control global y local, en lugar de emplear una única combinación predeterminada (como es el caso de la evaluación parcial tradicional). Este entorno es más flexible que los enfoques existentes, ya que permite asignar diferentes reglas de control local y global a diferentes patrones de llamada. De este modo, es posible obtener programas especializados que no pueden ser generados usando evaluación parcial tradicional. En consecuencia, el entorno de evaluación parcial policontrolada puede generar conjuntos de programas especializados, en lugar de un único programa. A través de técnicas auto-ajustables, es posible hacer que este enfoque sea completamente automático. Dichas técnicas permiten medir la calidad de los diferentes programas especializados obtenidos. Este entorno es consciente de los recursos, en el sentido de que cada una de las soluciones obtenidas a través de la evaluación parcial policontrolada es valorada utilizando funciones de adecuación, las que pueden tener en cuenta factores tales como el tamaño de los programas especializados, o la cantidad de memoria que consumen, además de la velocidad del programa especializado que es el factor habitualmente considerado en otros entornos de evaluación parcial. Este entorno de evaluación parcial policontrolada ha sido implementado en el sistema CiaoPP, y evaluado con numerosos programas de prueba. Los resultados experimentales muestran que nuestra propuesta obtiene en muchos casos mejores especializaciones que aquellas generadas usando la evaluación parcial tradicional, especialmente cuando la especialización es consciente de los recursos. Otra de las principales contribuciones de esta tesis es la presentación de una visión unificada del problema de eliminar la polivarianza superflua en la evaluación parcial y en la especialización abstracta múltiple, a través del uso de un paso de minimización, el cual agrupa versiones equivalentes de predicados. Este paso se puede aplicar en la especialización de cualquier programa Prolog, inclusive aquellos que contienen llamadas a predicados predefinidos o predicados externos. Además, ofrecemos la posibilidad de agrupar versiones que no sean estrictamente equivalentes, con el propósito de obtener programas más pequeños