Scaling up algorithmic debugging with virtual execution trees

Declarative debugging is a powerful debugging technique that has been adapted to practically all programming languages. However, the technique suffers from important scalability problems in both time and memory. With realistic programs the huge size of the execution tree handled makes the debugging session impractical and too slow to be productive. In this work, we present a new architecture for declarative debuggers in which we adapt the technique to work with incomplete execution trees. This allows us to avoid the problem of loading the whole execution tree in main memory and solve the memory scalability problems. We also provide the technique with the ability to debug execution trees that are only partially generated. This allows the programmer to start the debugging session even before the execution tree is computed. This solves the time scalability problems. We have implemented the technique and show its practicality with several experiments conducted with real applications.Insa Cabrera, D.; Silva Galiana, JF. (2011). Scaling up algorithmic debugging with virtual execution trees. En Logic-Based Program Synthesis and Transformation. Springer Verlag (Germany). 6564:149-163. doi:10.1007/978-3-642-20551-4_10S1491636564Av-Ron, E.: Top-Down Diagnosis of Prolog Programs. PhD thesis, Weizmanm Institute (1984)Binks, D.: Declarative Debugging in Gödel. PhD thesis, University of Bristol (1995)Caballero, R.: A Declarative Debugger of Incorrect Answers for Constraint Functional-Logic Programs. In: Proc. of the 2005 ACM SIGPLAN Workshop on Curry and Functional Logic Programming (WCFLP 2005), pp. 8–13. ACM Press, New York (2005)Caballero, R.: Algorithmic Debugging of Java Programs. In: Proc. of the 2006 Workshop on Functional Logic Programming (WFLP 2006). Electronic Notes in Theoretical Computer Science, pp. 63–76 (2006)Caballero, R., Martí-Oliet, N., Riesco, A., Verdejo, A.: A declarative debugger for maude functional modules. Electronic Notes Theoretical Computer Science 238(3), 63–81 (2009)Davie, T., Chitil, O.: Hat-delta: One Right Does Make a Wrong. In: Seventh Symposium on Trends in Functional Programming, TFP 2006 (April 2006)Girgis, H., Jayaraman, B.: JavaDD: a Declarative Debugger for Java. Technical Report 2006-07, University at Buffalo (March 2006)Kokai, G., Nilson, J., Niss, C.: GIDTS: A Graphical Programming Environment for Prolog. In: Workshop on Program Analysis For Software Tools and Engineering (PASTE 1999), pp. 95–104. ACM Press, New York (1999)MacLarty, I.: Practical Declarative Debugging of Mercury Programs. PhD thesis, Department of Computer Science and Software Engineering, The University of Melbourne (2005)Sun Microsystems. Java Platform Debugger Architecture - JPDA (2010), http://java.sun.com/javase/technologies/core/toolsapis/jpda/Nilsson, H., Fritzson, P.: Algorithmic Debugging for Lazy Functional Languages. Journal of Functional Programming 4(3), 337–370 (1994)Shapiro, E.Y.: Algorithmic Program Debugging. MIT Press, Cambridge (1982)Silva, J.: An Empirical Evaluation of Algorithmic Debugging Strategies. Technical Report DSIC-II/10/09, UPV (2009), http://www.dsic.upv.es/~jsilva/research.htm#techsSilva, J.: Algorithmic debugging strategies. In: Proc. of International Symposium on Logic-based Program Synthesis and Transformation (LOPSTR 2006), pp. 134–140 (2006)Silva, J.: A Comparative Study of Algorithmic Debugging Strategies. In: Puebla, G. (ed.) LOPSTR 2006. LNCS, vol. 4407, pp. 143–159. Springer, Heidelberg (2007

Optimal divide and query

Algorithmic debugging is a semi-automatic debugging technique that allows the programmer to precisely identify the location of bugs without the need to inspect the source code. The technique has been successfully adapted to all paradigms and mature implementations have been released for languages such as Haskell, Prolog or Java. During three decades, the algorithm introduced by Shapiro and later improved by Hirunkitti has been thought optimal. In this paper we first show that this algorithm is not optimal, and moreover, in some situations it is unable to find all possible solutions, thus it is incomplete. Then, we present a new version of the algorithm that is proven optimal, and we introduce some equations that allow the algorithm to identify all optimal solutions.This work has been partially supported by the Spanish Ministerio de Ciencia e Innovación under grant TIN2008-06622-C03-02 and by the Generalitat Valenciana under grant PROMETEO/2011/052.Insa Cabrera, D.; Silva Galiana, JF. (2011). Optimal divide and query. En Progress in Artificial Intelligence. Springer Verlag (Germany). 7026:224-238. https://doi.org/10.1007/978-3-642-24769-9_17S2242387026Braßel, B., Huch, F.: The Kiel Curry system KiCS. In: Proc of 17th International Conference on Applications of Declarative Programming and Knowledge Management (INAP 2007) and 21st Workshop on (Constraint) Logic Programming (WLP 2007), pp. 215–223. Technical Report 434, University of Würzburg (2007)Caballero, R.: A Declarative Debugger of Incorrect Answers for Constraint Functional-Logic Programs. In: Proc. of the 2005 ACM SIGPLAN Workshop on Curry and Functional Logic Programming (WCFLP 2005), pp. 8–13. ACM Press, New York (2005)Caballero, R.: Algorithmic Debugging of Java Programs. In: Proc. of the 2006 Workshop on Functional Logic Programming (WFLP 2006). Electronic Notes in Theoretical Computer Science, pp. 63–76 (2006)Caballero, R., Martí-Oliet, N., Riesco, A., Verdejo, A.: A Declarative Debugger for Maude Functional Modules. Electronic Notes in Theoretical Computer Science 238, 63–81 (2009)Davie, T., Chitil, O.: Hat-delta: One Right Does Make a Wrong. In: Seventh Symposium on Trends in Functional Programming, TFP 2006 (April 2006)Fritzson, P., Shahmehri, N., Kamkar, M., Gyimóthy, T.: Generalized Algorithmic Debugging and Testing. LOPLAS 1(4), 303–322 (1992)Hirunkitti, V., Hogger, C.J.: A Generalised Query Minimisation for Program Debugging. In: Adsul, B. (ed.) AADEBUG 1993. LNCS, vol. 749, pp. 153–170. Springer, Heidelberg (1993)Insa, D., Silva, J.: An Algorithmic Debugger for Java. In: Proc. of the 26th IEEE International Conference on Software Maintenance, pp. 1–6 (2010)Insa, D., Silva, J.: Optimal Divide and Query (extended version). Available in the Computing Research Repository (July 2011), http://arxiv.org/abs/1107.0350Lloyd, J.W.: Declarative Error Diagnosis. New Gen. Comput. 5(2), 133–154 (1987)Luo, Y., Chitil, O.: Algorithmic debugging and trusted functions. Technical report 10-07, University of Kent, Computing Laboratory, UK (August 2007)Lux, W.: Münster Curry User’s Guide (release 0.9.10 of May 10, 2006), http://danae.uni-muenster.de/~lux/curry/user.pdfMacLarty, I.: Practical Declarative Debugging of Mercury Programs. PhD thesis, Department of Computer Science and Software Engineering, The University of Melbourne (2005)Naish, L., Dart, P.W., Zobel, J.: The NU-Prolog Debugging Environment. In: Porto, A. (ed.) Proceedings of the Sixth International Conference on Logic Programming, Lisboa, Portugal, pp. 521–536 (June 1989)Nilsson, H.: Declarative Debugging for Lazy Functional Languages. PhD thesis, Linköping, Sweden (May 1998)Pope, B.: A Declarative Debugger for Haskell. PhD thesis, The University of Melbourne, Australia (2006)Shapiro, E.: Algorithmic Program Debugging. MIT Press (1982)Silva, J.: A Comparative Study of Algorithmic Debugging Strategies. In: Puebla, G. (ed.) LOPSTR 2006. LNCS, vol. 4407, pp. 143–159. Springer, Heidelberg (2007)Silva, J.: An Empirical Evaluation of Algorithmic Debugging Strategies. Technical Report DSIC-II/10/09, UPV (2009), http://www.dsic.upv.es/~jsilva/research.htm#tech

Towards a debugging tutor for object-oriented environments

Programming has provided a rich domain for Artificial Intelligence in Education and many systems have been developed to advise students about the bugs in their programs, either during program development or post-hoc. Surprisingly few systems have been developed specifically to teach debugging. Learning environment builders have assumed that either the student will be taught these elsewhere or thatthey will be learnt piecemeal without explicit advice.This paper reports on two experiments on Java debugging strategy by novice programmers and discusses their implications for the design of a debugging tutor for Java that pays particular attention to how students use the variety of program representations available. The experimental results are in agreement with research in the area that suggests that good debugging performance is associated with a balanced use ofthe available representations and a sophisticated use of the debugging step facility which enables programmers to detect and obtain information from critical momentsin the execution of the program. A balanced use of the available representations seemsto be fostered by providing representations with a higher degree of dynamic linkingas well as by explicit instruction about the representation formalism employed in the program visualisations

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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Ph.D. Dissertation. University of Minnesota.Tamarit, S., Riesco, A., Martin-Martin, E., & Caballero, R. (2016). Debugging Meets Testing in Erlang. Lecture Notes in Computer Science, 171-180. doi:10.1007/978-3-319-41135-4_10A. Tessier and G. Ferrand. 2000. Declarative diagnosis in the CLP scheme. In Analysis and Visualization Tools for Constraint Programming: Constraint Debugging Pierre Deransart Manuel V. Hermenegildo and Jan Maluszynski (Eds.). Springer-Verlag Berlin 151--174. 10.1007/10722311_6 A. Tessier and G. Ferrand. 2000. Declarative diagnosis in the CLP scheme. In Analysis and Visualization Tools for Constraint Programming: Constraint Debugging Pierre Deransart Manuel V. Hermenegildo and Jan Maluszynski (Eds.). Springer-Verlag Berlin 151--174. 10.1007/10722311_6Zinn, C. (2013). Algorithmic Debugging for Intelligent Tutoring: How to Use Multiple Models and Improve Diagnosis. Lecture Notes in Computer Science, 272-283. doi:10.1007/978-3-642-40942-4_24Zinn, C. (2014). 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A survey on algorithmic debugging strategies

Algorithmic debugging is a debugging technique that has been extended to practically all programming paradigms. Roughly speaking, the technique constructs an internal representation of all (sub)computations performed during the execution of a buggy program; and then, it asks the programmer about the correctness of such computations. The answers of the programmer guide the search for the bug until it is isolated by discarding correct parts of the program. After twenty years of research in algorithmic debugging many different techniques have appeared to improve the original proposal. Surprisingly, no study exists that joins together all these techniques and compares their advantages and their performance. This article presents a study that compares all current algorithmic debugging techniques and analyzes their differences and their costs. The research identifies the dimensions on which each strategy relies. This information allows us to combine the strong points of different strategies.This work has been partially supported by the Spanish Ministerio de Ciencia e Innovacion under Grant TIN2008-06622-C03-02 and by the Generalitat Valenciana under Grant PROMETEO/2011/052.Silva Galiana, JF. (2011). A survey on algorithmic debugging strategies. Advances in Engineering Software. 42(11):976-991. https://doi.org/10.1016/j.advengsoft.2011.05.024S976991421

Computational Thinking Self-Efficacy in High School Latin Language Learning

Research suggests that computational thinking is a necessary skill exercised in STEM courses, non-STEM fields, and in everyday life. However, very little research has investigated the potential transfer of computational thinking self-efficacy available through classical Latin courses. This causal comparative study contrasted the computational thinking self-efficacy of computer science students with no exposure to Latin to computer science students with exposure to Latin at a Memphis all-boy high school. The independent variables were Latin language learning experience, i.e., up to 6 years total of Latin language learning (n = 33), versus 0 years of Latin language learning experience (n = 20). Additional data on the number of years enrolled in other foreign languages was collected. The dependent variable was mean scores of items found on a computational thinking and problem solving self-efficacy scale. This instrument uses a Likert scale to measure students self-efficacy in nine computational thinking components including algorithmic thinking; abstraction; problem decomposition; data collection, representation, and analysis; parallelization; control flow; incremental and iterative; testing and debugging; and questioning. Conducting this research addressed the question of whether the computational thinking skills present in Latin can transfer to a students computational thinking self-efficacy which may affect STEM/computer science course achievement. To test the null hypothesis that having a Latin language learning yields no significant influence on computer science students self-efficacy in computational thinking and problem solving, a multivariate analysis of variance (MANOVA) test was utilized for this causal-comparative study. To test the null hypotheses that having a Latin language learning yields no significant influence on computer science students abstraction, problem decomposition, data, parallelization, control flow, incremental and iterative, testing and debugging, and questioning skills self-efficacy, a separate ANOVA test were run for each computational thinking skill component.The data did not meet of the necessary assumptions for a MANOVA test. The sample size for the non-Latin group was a concern at n = 20. The means from the descriptive statistics show that the non-Latin group outscored the Latin group in most of the computational thinking skills. Pillais trace statistic from the MANOVA test showed no statistical significance in the computational thinking and problem solving scale. The individual results from the ANOVA tests showed no statistical significance for any of the nine subscales

An Analysis of the Current Program Slicing and Algorithmic Debugging Based Techniques

This thesis presents a classification of program slicing based techniques. The classification allows us to identify the differences between existing techniques, but it also allows us to predict new slicing techniques. The study identifies and compares the dimensions that influence current techniques.Silva Galiana, JF. (2008). An Analysis of the Current Program Slicing and Algorithmic Debugging Based Techniques. http://hdl.handle.net/10251/14300Archivo delegad

Contract-Based General-Purpose GPU Programming

Using GPUs as general-purpose processors has revolutionized parallel computing by offering, for a large and growing set of algorithms, massive data-parallelization on desktop machines. An obstacle to widespread adoption, however, is the difficulty of programming them and the low-level control of the hardware required to achieve good performance. This paper suggests a programming library, SafeGPU, that aims at striking a balance between programmer productivity and performance, by making GPU data-parallel operations accessible from within a classical object-oriented programming language. The solution is integrated with the design-by-contract approach, which increases confidence in functional program correctness by embedding executable program specifications into the program text. We show that our library leads to modular and maintainable code that is accessible to GPGPU non-experts, while providing performance that is comparable with hand-written CUDA code. Furthermore, runtime contract checking turns out to be feasible, as the contracts can be executed on the GPU

Optimization Techniques for Algorithmic Debugging

[EN] Nowadays, undetected programming bugs produce a waste of billions of dollars per year to private and public companies and institutions. In spite of this, no significant advances in the debugging area that help developers along the software development process have been achieved yet. In fact, the same debugging techniques that were used 20 years ago are still being used now. Along the time, some alternatives have appeared, but there still is a long way for them to be useful enough to get into the software development process. One of them is algorithmic debugging, which abstracts the information the user has to investigate to debug the program, allowing them to focus on what is happening instead of how it is happening. This abstraction comes at a price: the granularity level of the bugs that can be detected allows for isolating wrongly implemented functions, but which part of them contains the bug cannot be found out yet. This thesis focusses on improving algorithmic debugging in many aspects. Concretely, the main aims of this thesis are to reduce the time the user needs to detect a programming bug as well as to provide the user with more detailed information about where the bug is located. To achieve these goals, some techniques have been developed to start the debugging sessions as soon as possible, to reduce the number of questions the user is going to be asked about, and to augment the granularity level of those bugs that algorithmic debugging can detect, allowing the debugger in this way to keep looking for bugs even inside functions. As a result of this thesis, three completely new techniques have been defined, an already existent technique has been improved, and two new algorithmic debugging search strategies have been defined that improve the already existent ones. Besides these theoretical results, a fully functional algorithmic debugger has been implemented that contains and supports all these techniques and strategies. This debugger is written in Java, and it debugs Java code. The election of this language is justified because it is currently one of the most widely extended and used languages. Also because it contains an interesting combination of unsolved challenges for algorithmic debugging. To further increase its usability, the debugger has been later adapted as an Eclipse plugin, so it could be used by a wider number of users. These two debuggers are publicly available, so any interested person can access them and continue with the research if they wish so.[ES] Hoy en día, los errores no detectados de programación suponen un gasto de miles de millones al año para las empresas e instituciones públicas y privadas. A pesar de esto, no ha habido ningún avance significativo en el área de la depuración que ayude a los desarrolladores durante la fase de desarrollo de software. De hecho, las mismas técnicas de depuración que se utilizaban hace 20 años se siguen utilizando ahora. A lo largo del tiempo, han surgido algunas alternativas, pero todavía queda un largo camino para que estas sean lo suficientemente útiles como para abrirse camino en el proceso de desarrollo de software. Una de ellas es la depuración algorítmica, la cual abstrae la información que el programador debe investigar para depurar el programa, permitiéndole de este modo centrarse en el qué está ocurriendo en vez de en el cómo. Esta abstracción tiene un coste: el nivel de granularidad de los errores que pueden detectarse nos permite como máximo aislar funciones mal implementadas, pero no averiguar qué parte de estas contiene el error. Esta tesis se centra en mejorar la depuración algorítmica en muchos aspectos. Concretamente, los principales objetivos de esta tesis son reducir el tiempo que el usuario necesita para detectar un error de programación así como proporcionar información más detallada de dónde se encuentra el error. Para conseguir estos objetivos, se han desarrollado técnicas para iniciar las sesiones de depuración lo antes posible, reducir el número de preguntas que se le van a realizar al usuario, y aumentar el nivel de granularidad de los errores que la depuración algorítmica puede detectar, permitiendo así seguir buscando el error incluso dentro de las funciones. Como resultado de esta tesis, se han definido tres técnicas completamente nuevas, se ha mejorado una técnica ya existente, y se han definido dos nuevas estrategias de depuración algorítmica que mejoran las previamente existentes. Además de los resultados teóricos, también se ha desarrollado un depurador algorítmico completamente funcional que contiene y respalda todas estas técnicas y estrategias. Este depurador está escrito en Java y depura código Java. La elección de este lenguaje se justifica debido a que es uno de los lenguajes más ampliamente extendidos y usados actualmente. También debido a que contiene una combinación interesante de retos todavía sin resolver para la depuración algorítmica. Para aumentar todavía más su usabilidad, el depurador ha sido posteriormente adaptado como un plugin de Eclipse, de tal manera que pudiese ser usado por un número más amplio de usuarios. Estos dos depuradores están públicamente disponibles para que cualquier persona interesada pueda acceder a ellos y continuar con la investigación si así lo deseara.[CA] Hui en dia, els errors no detectats de programació suposen una despesa de milers de milions a l'any per a les empreses i institucions públiques i privades. Tot i això, no hi ha hagut cap avanç significatiu en l'àrea de la depuració que ajude als desenvolupadors durant la fase de desenvolupament de programari. De fet, les mateixes tècniques de depuració que s'utilitzaven fa 20 anys es continuen utilitzant ara. Al llarg del temps, han sorgit algunes alternatives, però encara queda un llarg camí perquè estes siguen prou útils com per a obrir-se camí en el procés de desenvolupament de programari. Una d'elles és la depuració algorítmica, la qual abstrau la informació que el programador ha d'investigar per a depurar el programa, permetent-li d'esta manera centrar-se en el què està ocorrent en compte de en el com. Esta abstracció té un cost: el nivell de granularitat dels errors que poden detectar-se ens permet com a màxim aïllar funcions mal implementades, però no esbrinar quina part d'estes conté l'error. Esta tesi es centra a millorar la depuració algorítmica en molts aspectes. Concretament, els principals objectius d'esta tesi són reduir el temps que l'usuari necessita per a detectar un error de programació així com proporcionar informació més detallada d'on es troba l'error. Per a aconseguir estos objectius, s'han desenvolupat tècniques per a iniciar les sessions de depuració com més prompte millor, reduir el nombre de preguntes que se li formularan a l'usuari, i augmentar el nivell de granularitat dels errors que la depuració algorítmica pot detectar, permetent així continuar buscant l'error inclús dins de les funcions. Com resultat d'esta tesi, s'han definit tres tècniques completament noves, s'ha millorat una tècnica ja existent, i s'han definit dos noves estratègies de depuració algorítmica que milloren les prèviament existents. A més dels resultats teòrics, també s'ha desenvolupat un depurador algorítmic completament funcional que conté i protegix totes estes tècniques i estratègies. Este depurador està escrit en Java i depura codi Java. L'elecció d'este llenguatge es justifica pel fet que és un dels llenguatges més àmpliament estesos i usats actualment. També pel fet que conté una combinació interessant de reptes encara sense resoldre per a la depuració algorítmica. Per a augmentar encara més la seua usabilitat, el depurador ha sigut posteriorment adaptat com un plugin d'Eclipse, de tal manera que poguera ser usat per un nombre més ampli d'usuaris. Estos dos depuradors estan públicament disponibles perquè qualsevol persona interessada puga accedir a ells i continuar amb la investigació si així ho desitjara.Insa Cabrera, D. (2016). Optimization Techniques for Algorithmic Debugging [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/68506TESISPremios Extraordinarios de tesis doctorale

Implementation of an optimal strategy for algorithmic debugging

[EN] One of the most automatic debugging techniques is Algorithmic Debugging because it allows us to debug a program without the need to inspect the source code. In order to find a bug, an algorithmic debugger asks questions to the programmer about the correctness of subcomputations in an execution. Reducing the number and complexity of these questions is an old objective in this field. Recently, an strategy for algorithmic debuggers that minimizes the number of questions has been released. This new strategy is called Optimal Divide and Query and, provided that all questions can be answered, it finds any bug in the source code with a minimum set of questions. In this work we discuss the implementation of such a strategy in different algorithmic debugging architectures. © 2011 Elsevier B.V. All rights reserved.This work has been partially supported by the Spanish Ministerio de Ciencia e Innovación (Secretar´ıa de Estado de Investigaci´on) under grant TIN2008-06622-C03-02 and by the Generalitat Valenciana under grant PROMETEO/2011/052Insa Cabrera, D.; Silva Galiana, JF. (2012). Implementation of an optimal strategy for algorithmic debugging. Electronic Notes in Theoretical Computer Science. 282:47-60. https://doi.org/10.1016/j.entcs.2011.12.005S476028