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

Speeding Up Algorithmic Debugging Using Balanced Execution Trees

Algorithmic debugging is a debugging technique that uses a data structure representing all computations performed during the execution of a program. This data structure is the so-called Execution Tree and it strongly influences the performance of the technique. In this work we present a transformation that automatically improves the structure of the execution trees by collapsing and projecting some strategic nodes. This improvement in the structure implies a better behavior and performance of the standard algorithms that traverse it. We prove that the transformation is sound in the sense that all the bugs found after the transformation are real bugs; and if at least one bug is detectable before the transformation, then at least one bug will also be detectable after the transformation. We have implemented the technique and performed several experiments with real applications. The experimental results confirm the usefulness of the technique.This work has been partially supported by the Spanish Ministerio de Ciencia e Innovación under grants TIN2008-06622-C03-02 and TIN2012-39391-004-04, by the Generalitat Valenciana under grant ACOMP/2009/017, and by the Comunidad de Madrid under grant S2009/TIC-1465. David Insa has been partially supported by the Spanish Ministerio de Educación under grant AP2010-4415.Silva, J.; Insa Cabrera, D.; Riesco, A. (2013). Speeding Up Algorithmic Debugging Using Balanced Execution Trees. En Tests and Proofs. Springer. 133-151. https://doi.org/10.1007/978-3-642-38916-0_8S133151Binks, 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 (2005)Caballero, R., Hermanns, C., Kuchen, H.: Algorithmic debugging of Java programs. In: López-Fraguas, F.J. (ed.) Proc. of the 15th Workshop on Functional and (Constraint) Logic Programming, WFLP 2006, Madrid, Spain. ENTCS, vol. 177, pp. 75–89. Elsevier (2007)Calejo, M.: A Framework for Declarative Prolog Debugging. PhD thesis, New University of Lisbon (1992)Davie, T., Chitil, O.: Hat-delta: One Right Does Make a Wrong. In: Seventh Symposium on Trends in Functional Programming, TFP 2006 (April 2006)Hirunkitti, V., Hogger, C.J.: A Generalised Query Minimisation for Program Debugging. In: Fritzson, P.A. (ed.) AADEBUG 1993. LNCS, vol. 749, pp. 153–170. Springer, Heidelberg (1993)Insa, D., Silva, J.: Scaling up algorithmic debugging with virtual execution trees. In: Alpuente, M. (ed.) LOPSTR 2010. LNCS, vol. 6564, pp. 149–163. Springer, Heidelberg (2011)Insa, D., Silva, J., Riesco, A.: Speeding up algorithmic debugging using balanced execution trees—detailed results. Technical Report 04/13, Departamento de Sistemas Informáticos y Computación (April 2013)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 (1999)MacLarty, I.: Practical Declarative Debugging of Mercury Programs. PhD thesis, Department of Computer Science and Software Engineering, University of Melbourne (2005)Maeji, M., Kanamori, T.: Top-Down Zooming Diagnosis of Logic Programs. Technical Report TR-290, Japan (1987)Nilsson, H.: Declarative Debugging for Lazy Functional Languages. PhD thesis, Linköping, Sweden (May 1998)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 (1982)Silva, J.: A Survey on Algorithmic Debugging Strategies. Advances in Engineering Software 42(11), 976–991 (2011

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

A declarative debugger of incorrect answers for constraint functional-logic programs

Debugging is one of the essential parts of the software development cycle. However, the usual debugging techniques used in imperative languages such as the step by step execution often are not suitable for debugging declarative programming languages. We present here a graphical debugging environment for constraint lazy functional-logic programs based on declarative debugging. The debugger dis-plays the computation tree associated with a computation which has produced an incorrect answer, and navigates it with the assistance of the user until the error, an incorrect program rule, is found out. The debugger supports programs including equality and disequality constraints

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). Debugging of wrong and missing answers for datalog programs with constraint handling rules. Proceedings of the 17th International Symposium on Principles and Practice of Declarative Programming - PPDP ’15. doi:10.1145/2790449.2790522Caballero, R., Martin-Martin, E., Riesco, A., & Tamarit, S. (2015). A zoom-declarative debugger for sequential Erlang programs. Science of Computer Programming, 110, 104-118. doi:10.1016/j.scico.2015.06.011Caballero, R., & Rodríguez-Artalejo, M. (2002). A Declarative Debugging System for Lazy Functional Logic Programs. Electronic Notes in Theoretical Computer Science, 64, 113-175. doi:10.1016/s1571-0661(04)80349-9Ceri, S., Gottlob, G., & Tanca, L. (1989). What you always wanted to know about Datalog (and never dared to ask). IEEE Transactions on Knowledge and Data Engineering, 1(1), 146-166. doi:10.1109/69.43410Chen, M., Mao, S., & Liu, Y. (2014). Big Data: A Survey. Mobile Networks and Applications, 19(2), 171-209. doi:10.1007/s11036-013-0489-0Chitil, O., & Davie, T. (2008). Comprehending finite maps for algorithmic debugging of higher-order functional programs. Proceedings of the 10th international ACM SIGPLAN symposium on Principles and practice of declarative programming - PPDP ’08. doi:10.1145/1389449.1389475Chitil, O., Faddegon, M., & Runciman, C. (2016). A Lightweight Hat. Proceedings of the 28th Symposium on the Implementation and Application of Functional Programming Languages - IFL 2016. doi:10.1145/3064899.3064904O. Chitil C. Runciman and M. Wallace. 2001. Freja Hat and Hood—A Comparative Evaluation of Three Systems for Tracing and Debugging Lazy Functional Programs. Springer Berlin 176--193. O. Chitil C. Runciman and M. Wallace. 2001. Freja Hat and Hood—A Comparative Evaluation of Three Systems for Tracing and Debugging Lazy Functional Programs. Springer Berlin 176--193.O. Chitil C. Runciman and Malcolm Wallace. 2003. Transforming Haskell for Tracing. Springer-Verlag Berlin 165--181. DOI:http://dx.doi.org/10.1007/3-540-44854-3_11 10.1007/3-540-44854-3_11 O. Chitil C. Runciman and Malcolm Wallace. 2003. Transforming Haskell for Tracing. Springer-Verlag Berlin 165--181. DOI:http://dx.doi.org/10.1007/3-540-44854-3_11 10.1007/3-540-44854-3_11Minh Ngoc Dinh, Abramson, D., & Chao Jin. (2014). Scalable Relative Debugging. IEEE Transactions on Parallel and Distributed Systems, 25(3), 740-749. doi:10.1109/tpds.2013.86Faddegon, M., & Chitil, O. (2015). Algorithmic debugging of real-world haskell programs: deriving dependencies from the cost centre stack. ACM SIGPLAN Notices, 50(6), 33-42. doi:10.1145/2813885.2737985Faddegon, M., & Chitil, O. (2016). Lightweight computation tree tracing for lazy functional languages. Proceedings of the 37th ACM SIGPLAN Conference on Programming Language Design and Implementation - PLDI 2016. doi:10.1145/2908080.2908104Ferrand, G. (1987). Error diagnosis in logic programming an adaptation of E.Y. Shapiro’s method. The Journal of Logic Programming, 4(3), 177-198. doi:10.1016/0743-1066(87)90001-xFritzson, P., Shahmehri, N., Kamkar, M., & Gyimothy, T. (1992). Generalized algorithmic debugging and testing. ACM Letters on Programming Languages and Systems, 1(4), 303-322. doi:10.1145/161494.161498Fromherz, M. P. J. (s. f.). Towards declarative debugging of concurrent constraint programs. Lecture Notes in Computer Science, 88-100. doi:10.1007/bfb0019403Harman, M., & Hierons, R. (2001). An overview of program slicing. Software Focus, 2(3), 85-92. doi:10.1002/swf.41F. Henderson T. Conway Z. Somogyi D. Jeffery P. Schachte S. Taylor C. Speirs T. Dowd R. Becket M. Brown and P. Wang. 2014. The Mercury Language Reference Manual (Version 14.01.1). The University of Melbourne. F. Henderson T. Conway Z. Somogyi D. Jeffery P. Schachte S. Taylor C. Speirs T. Dowd R. Becket M. Brown and P. Wang. 2014. The Mercury Language Reference Manual (Version 14.01.1). The University of Melbourne.C. Hermanns and H. Kuchen. 2013. Hybrid Debugging of Java Programs. Springer-Verlag Berlin 91--107. DOI:http://dx.doi.org/10.1007/978-3-642-36177-7_6 10.1007/978-3-642-36177-7_6 C. Hermanns and H. Kuchen. 2013. Hybrid Debugging of Java Programs. Springer-Verlag Berlin 91--107. DOI:http://dx.doi.org/10.1007/978-3-642-36177-7_6 10.1007/978-3-642-36177-7_6Hirunkitti, V., & Hogger, C. J. (s. f.). A generalised query minimisation for program debugging. Lecture Notes in Computer Science, 153-170. doi:10.1007/bfb0019407Hughes, J. (2010). Software Testing with QuickCheck. Lecture Notes in Computer Science, 183-223. doi:10.1007/978-3-642-17685-2_6G. Hutton. 2016. Programming in Haskell. Cambridge University Press Cambridge UK. G. Hutton. 2016. Programming in Haskell. Cambridge University Press Cambridge UK.Insa, D., & Silva, J. (2010). An algorithmic debugger for Java. 2010 IEEE International Conference on Software Maintenance. doi:10.1109/icsm.2010.5609661Insa, D., & Silva, J. (2011). Optimal Divide and Query. Lecture Notes in Computer Science, 224-238. doi:10.1007/978-3-642-24769-9_17Insa, D., & Silva, J. (2011). An optimal strategy for algorithmic debugging. 2011 26th IEEE/ACM International Conference on Automated Software Engineering (ASE 2011). doi:10.1109/ase.2011.6100055D. Insa and J. Silva. 2011c. Scaling Up Algorithmic Debugging with Virtual Execution Trees. Springer-Verlag Berlin 149--163. DOI:http://dx.doi.org/10.1007/978-3-642-20551-4_10 10.1007/978-3-642-20551-4_10 D. Insa and J. Silva. 2011c. Scaling Up Algorithmic Debugging with Virtual Execution Trees. Springer-Verlag Berlin 149--163. DOI:http://dx.doi.org/10.1007/978-3-642-20551-4_10 10.1007/978-3-642-20551-4_10D. Insa and J. Silva. 2015a. Automatic transformation of iterative loops into recursive methods. Information 8 Software Technology 58 (2015) 95--109. DOI:http://dx.doi.org/10.1016/j.infsof.2014.10.001 10.1016/j.infsof.2014.10.001 D. Insa and J. Silva. 2015a. Automatic transformation of iterative loops into recursive methods. Information 8 Software Technology 58 (2015) 95--109. DOI:http://dx.doi.org/10.1016/j.infsof.2014.10.001 10.1016/j.infsof.2014.10.001Insa, D., & Silva, J. (2015). A Generalized Model for Algorithmic Debugging. Lecture Notes in Computer Science, 261-276. doi:10.1007/978-3-319-27436-2_16Insa, D., Silva, J., & Riesco, A. (2013). Speeding Up Algorithmic Debugging Using Balanced Execution Trees. Lecture Notes in Computer Science, 133-151. doi:10.1007/978-3-642-38916-0_8Insa, D., Silva, J., & Tomás, C. (2013). Enhancing Declarative Debugging with Loop Expansion and Tree Compression. Lecture Notes in Computer Science, 71-88. doi:10.1007/978-3-642-38197-3_6K. Jensen and N. Wirth. 1974. PASCAL User Manual and Report. Springer-Verlag Berlin. 10.1007/978-3-662-21554-8 K. Jensen and N. Wirth. 1974. PASCAL User Manual and Report. Springer-Verlag Berlin. 10.1007/978-3-662-21554-8Jia, Y., & Harman, M. (2011). An Analysis and Survey of the Development of Mutation Testing. IEEE Transactions on Software Engineering, 37(5), 649-678. doi:10.1109/tse.2010.62Kamkar, M., Shahmehri, N., & Fritzson, P. (s. f.). Bug localization by algorithmic debugging and program slicing. Lecture Notes in Computer Science, 60-74. doi:10.1007/bfb0024176S. Köhler B. Ludäscher and Y. Smaragdakis. 2012. Declarative Datalog Debugging for Mere Mortals. Springer-Verlag Berlin 111--122. S. Köhler B. Ludäscher and Y. Smaragdakis. 2012. Declarative Datalog Debugging for Mere Mortals. Springer-Verlag Berlin 111--122.Kouh, H.-J., & Yoo, W.-H. (2003). The Efficient Debugging System for Locating Logical Errors in Java Programs. Lecture Notes in Computer Science, 684-693. doi:10.1007/3-540-44839-x_72Benzmüller, C., & Miller, D. (2014). Automation of Higher-Order Logic. Handbook of the History of Logic, 215-254. doi:10.1016/b978-0-444-51624-4.50005-8Kowalski, R., & Kuehner, D. (1971). Linear resolution with selection function. Artificial Intelligence, 2(3-4), 227-260. doi:10.1016/0004-3702(71)90012-9K. Kuchcinski W. Drabent and J. Maluszynski. 1993. Automatic Diagnosis of VLSI Digital Circuits Using Algorithmic Debugging. Springer-Verlag Berlin 350--367. DOI:http://dx.doi.org/10.1007/BFb0019419 10.1007/BFb0019419 K. Kuchcinski W. Drabent and J. Maluszynski. 1993. Automatic Diagnosis of VLSI Digital Circuits Using Algorithmic Debugging. Springer-Verlag Berlin 350--367. DOI:http://dx.doi.org/10.1007/BFb0019419 10.1007/BFb0019419S. Liang. 1999. Java Native Interface: Programmer’s Guide and Reference (1st ed.). Addison-Wesley Longman Publishing Co. Inc. Boston MA. S. Liang. 1999. Java Native Interface: Programmer’s Guide and Reference (1st ed.). Addison-Wesley Longman Publishing Co. Inc. Boston MA.Lloyd, J. W. (1987). Declarative error diagnosis. New Generation Computing, 5(2), 133-154. doi:10.1007/bf03037396J. W. Lloyd. 1987b. Foundations of Logic Programming (2nd ed.). Springer-Verlag Berlin. 10.1007/978-3-642-83189-8 J. W. Lloyd. 1987b. Foundations of Logic Programming (2nd ed.). Springer-Verlag Berlin. 10.1007/978-3-642-83189-8W. Lux. 2006. Münster Curry User’s guide (Release 0.9.10 of May 10 2006). Retrieved from http://danae.uni-muenster.de/∼lux/curry/user.pdf. W. Lux. 2006. Münster Curry User’s guide (Release 0.9.10 of May 10 2006). Retrieved from http://danae.uni-muenster.de/∼lux/curry/user.pdf.Lux, W. (2008). Declarative Debugging Meets the World. Electronic Notes in Theoretical Computer Science, 216, 65-77. doi:10.1016/j.entcs.2008.06.034I. MacLarty. 2005. Practical Declarative Debugging of Mercury Programs. Ph.D. Dissertation. Department of Computer Science and Software Engineering The University of Melbourne. I. MacLarty. 2005. Practical Declarative Debugging of Mercury Programs. Ph.D. Dissertation. Department of Computer Science and Software Engineering The University of Melbourne.Naganuma, J., Ogura, T., & Hoshino, T. (s. f.). High-level design validation using algorithmic debugging. Proceedings of European Design and Test Conference EDAC-ETC-EUROASIC. doi:10.1109/edtc.1994.326833Naish, L. (1992). Declarative diagnosis of missing answers. New Generation Computing, 10(3), 255-285. doi:10.1007/bf03037939H. Nilsson. 1998. Declarative Debugging for Lazy Functional Languages. Ph.D. Dissertation. Linköping Sweden. H. Nilsson. 1998. Declarative Debugging for Lazy Functional Languages. Ph.D. Dissertation. Linköping Sweden.NILSSON, H. (2001). How to look busy while being as lazy as ever: the Implementation of a lazy functional debugger. Journal of Functional Programming, 11(6), 629-671. doi:10.1017/s095679680100418xNilsson, H., & Fritzson, P. (s. f.). Algorithmic debugging for lazy functional languages. Lecture Notes in Computer Science, 385-399. doi:10.1007/3-540-55844-6_149Nilsson, H., & Fritzson, P. (1994). Algorithmic debugging for lazy functional languages. Journal of Functional Programming, 4(3), 337-369. doi:10.1017/s095679680000109xNilsson, H., & Sparud, J. (1997). Automated Software Engineering, 4(2), 121-150. doi:10.1023/a:1008681016679Ostrand, T. J., & Balcer, M. J. (1988). The category-partition method for specifying and generating fuctional tests. Communications of the ACM, 31(6), 676-686. doi:10.1145/62959.62964Pereira, L. M. (1986). Rational debugging in logic programming. Third International Conference on Logic Programming, 203-210. doi:10.1007/3-540-16492-8_76B. Pope. 2006. A Declarative Debugger for Haskell. Ph.D. Dissertation. The University of Melbourne Australia. B. Pope. 2006. A Declarative Debugger for Haskell. Ph.D. Dissertation. The University of Melbourne Australia.Ramakrishnan, R., & Ullman, J. D. (1995). A survey of deductive database systems. The Journal of Logic Programming, 23(2), 125-149. doi:10.1016/0743-1066(94)00039-9Riesco, A., Verdejo, A., Martí-Oliet, N., & Caballero, R. (2012). Declarative debugging of rewriting logic specifications. The Journal of Logic and Algebraic Programming, 81(7-8), 851-897. doi:10.1016/j.jlap.2011.06.004DeRose, L., Gontarek, A., Vose, A., Moench, R., Abramson, D., Dinh, M. N., & Jin, C. (2015). Relative debugging for a highly parallel hybrid computer system. Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis on - SC ’15. doi:10.1145/2807591.2807605Runeson, P. (2006). A survey of unit testing practices. IEEE Software, 23(4), 22-29. doi:10.1109/ms.2006.91Russo, F., & Sancassani, M. (1992). A declarative debugging environment for DATALOG. Lecture Notes in Computer Science, 433-441. doi:10.1007/3-540-55460-2_32E. Y. Shapiro. 1982a. Algorithmic Program Debugging. MIT Press Cambridge MA. E. Y. Shapiro. 1982a. Algorithmic Program Debugging. MIT Press Cambridge MA.Shapiro, E. Y. (1982). Algorithmic program diagnosis. Proceedings of the 9th ACM SIGPLAN-SIGACT symposium on Principles of programming languages - POPL ’82. doi:10.1145/582153.582185Shmueli, O., & Tsur, S. (1991). Logical diagnosis ofLDL programs. New Generation Computing, 9(3-4), 277-303. doi:10.1007/bf03037166Silva, J. (s. f.). A Comparative Study of Algorithmic Debugging Strategies. Lecture Notes in Computer Science, 143-159. doi:10.1007/978-3-540-71410-1_11Silva, J. (2011). A survey on algorithmic debugging strategies. Advances in Engineering Software, 42(11), 976-991. doi:10.1016/j.advengsoft.2011.05.024Silva, J., & Chitil, O. (2006). Combining algorithmic debugging and program slicing. Proceedings of the 8th ACM SIGPLAN symposium on Principles and practice of declarative programming - PPDP ’06. doi:10.1145/1140335.1140355J. A. Silva E. R. Faria R. C. Barros E. R. Hruschka A. C. P. L. F. de Carvalho and J. Gama. 2013. Data stream clustering: A survey. Comput. Surv. 46 1 Article 13 (July 2013) 31 pages.DOI:http://dx.doi.org/10.1145/2522968.2522981 10.1145/2522968.2522981 J. A. Silva E. R. Faria R. C. Barros E. R. Hruschka A. C. P. L. F. de Carvalho and J. Gama. 2013. Data stream clustering: A survey. Comput. Surv. 46 1 Article 13 (July 2013) 31 pages.DOI:http://dx.doi.org/10.1145/2522968.2522981 10.1145/2522968.2522981SOSIČ, R., & ABRAMSON, D. (1997). Guard: A Relative Debugger. Software: Practice and Experience, 27(2), 185-206. doi:10.1002/(sici)1097-024x(199702)27:23.0.co;2-dL. Sterling and E. Shapiro. 1986. The Art of Prolog: Advanced Programming Techniques. The MIT Press Cambridge MA. L. Sterling and E. Shapiro. 1986. The Art of Prolog: Advanced Programming Techniques. The MIT Press Cambridge MA.P. Kambam Sugavanam. 2013. Debugging Framework for Attribute Grammars. Ph.D. Dissertation. University of Minnesota. P. Kambam Sugavanam. 2013. Debugging Framework for Attribute Grammars. 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). Algorithmic Debugging and Literate Programming to Generate Feedback in Intelligent Tutoring Systems. KI 2014: Advances in Artificial Intelligence, 37-48. doi:10.1007/978-3-319-11206-0_

A Generalized Model for Algorithmic Debugging

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-27436-2_16Algorithmic debugging is a semi-automatic debugging technique that is present in practically all mature programming languages. In this paper we claim that the state of the practice in algorithmic debugging is a step forward compared to the state of the theory. In particular, we argue that novel techniques for algorithmic debugging cannot be supported by the standard internal data structures used in this technique, and a generalization of the standard definitions and algorithms is needed. We identify two specific problems of the standard formulation and implementations of algorithmic debugging, and we propose a reformulation to solve both problems. The reformulation has been done in a paradigm-independent manner to make it useful and reusable in different programming languages.This work has been partially supported by the EU (FEDER) and the Spanish Ministerio de Economía y Competitividad (Secretaría de Estado de Investigación, Desarrollo e Innovación) under Grant TIN2013-44742-C4-1-R and by the Generalitat Valenciana under Grant PROMETEOII/2015/013. David Insa was partially supported by the Spanish Ministerio de Educación under FPU Grant AP2010-4415.Insa Cabrera, D.; Silva Galiana, JF. (2015). A Generalized Model for Algorithmic Debugging. En Logic-Based Program Synthesis and Transformation. Springer. 261-276. https://doi.org/10.1007/978-3-319-27436-2_16261276Eclipse (2003). http://www.eclipse.org/Barbour, T., Naish, L.: Declarative debugging of a logical-functional language. Technical report, University of Melbourne (1994)Braßel, B., Siegel, H.: Debugging lazy functional programs by asking the oracle. In: Chitil, O., Horváth, Z., Zsók, V. (eds.) IFL 2007. LNCS, vol. 5083, pp. 183–200. Springer, Heidelberg (2008)Caballero, R.: A declarative debugger of incorrect answers for constraint functional-logic programs. In: Proceedings of the 2005 ACM-SIGPLAN Workshop on Curry and Functional Logic Programming (WCFLP 2005), pp. 8–13. ACM Press, New York, USA (2005)Caballero, R., Martin-Martin, E., Riesco, A., Tamarit, S.: EDD: A declarative debugger for sequential erlang programs. In: Ábrahám, E., Havelund, K. (eds.) TACAS 2014 (ETAPS). LNCS, vol. 8413, pp. 581–586. Springer, Heidelberg (2014)Caballero, R., Riesco, A., Verdejo, A., Martí-Oliet, N.: Simplifying questions in maude declarative debugger by transforming proof trees. In: Vidal, G. (ed.) LOPSTR 2011. LNCS, vol. 7225, pp. 73–89. Springer, Heidelberg (2012)Cheda, D., Silva, J.: State of the practice in algorithmic debugging. Electron. Notes Theor. Comput. Sci. 246, 55–70 (2009)Davie, T., Chitil, O.: Hat-delta: one right does make a wrong. In: Butterfield, A., (ed.) Proceedings of the 17th International Workshop on Implementation and Application of Functional Languages (IFL 2005), p. 11, September 2005Davie, T., Chitil, O.: Hat-delta: One right does make a wrong. In: Proceedings of the 7th Symposium on Trends in Functional Programming (TFP 2006), April 2006Fritzson, P., Shahmehri, N., Kamkar, M., Gyimóthy, T.: Generalized algorithmic debugging and testing. ACM Lett. Program. Lang. Syst. (LOPLAS) 1(4), 303–322 (1992)González, J., Insa, D., Silva, J.: A new hybrid debugging architecture for eclipse. In: Gupta, G., Peña, R. (eds.) LOPSTR 2013, LNCS 8901. LNCS, vol. 8901, pp. 183–201. Springer, Heidelberg (2014)Hermanns, C., Kuchen, H.: Hybrid debugging of java programs. In: Escalona, M.J., Cordeiro, J., Shishkov, B. (eds.) ICSOFT 2011. CCIS, vol. 303, pp. 91–107. Springer, Heidelberg (2013)Insa, D., Silva, J.: An algorithmic debugger for java. In: Proceedings of the 26th IEEE International Conference on Software Maintenance (ICSM 2010), pp. 1–6 (2010)Insa, D., Silva, J.: Automatic transformation of iterative loops into recursive methods. Inf. Soft. Technol. 58, 95–109 (2015)Insa, D., Silva, J., Riesco, A.: Speeding up algorithmic debugging using balanced execution trees. In: Veanes, M., Viganò, L. (eds.) TAP 2013. LNCS, vol. 7942, pp. 133–151. Springer, Heidelberg (2013)Insa, D., Silva, J., Tomás, C.: Enhancing declarative debugging with loop expansion and tree compression. In: Albert, E. (ed.) LOPSTR 2012. LNCS, vol. 7844, pp. 71–88. Springer, Heidelberg (2013)Lloyd, J.: Declarative error diagnosis. New Gener. Comput. 5(2), 133–154 (1987)Lux, M.: Münster Curry User’s Guide, May 2006. http://danae.uni-muenster.de/lux/curry/user.pdf ,MacLarty, I.D.: Practical Declarative Debugging of Mercury Programs. Ph.D. thesis, University of Melbourne (2005)Naish, L., Dart, P.W., Zobel, J.: The NU-Prolog debugging environment. In: Porto, A. (ed.) Proceedings of the 6th International Conference on Logic Programming (ICLP 1989), pp. 521–536. Lisboa, Portugal (1989)Nilsson, H.: Declarative Debugging for Lazy Functional Languages. Ph.D. thesis, Linköping, Sweden, May 1998Nilsson, H.: How to look busy while being as lazy as ever: the implementation of a lazy functional debugger. J. Funct. Program. 11(6), 629–671 (2001)Nilsson, H., Fritzson, P.: Algorithmic debugging for lazy functional languages. J. Funct. Program. 4(3), 337–370 (1994)Nilsson, H., Sparud, J.: The evaluation dependence tree: an execution record for lazy functional debugging. Technical report, Department of Computer and Information Science, Linköping (1996)Nilsson, H., Sparud, J.: The evaluation dependence tree as a basis for lazy functional debugging. Autom. Softw. Eng. 4(2), 121–150 (1997)Pope, B.: A Declarative Debugger for Haskell. Ph.D. thesis, The University of Melbourne, Australia (2006)Shapiro, E.: Algorithmic Program Debugging. MIT Press, Cambridge (1982)Shapiro, E.Y.: Inductive inference of theories from facts. Technical report RR 192, Yale University (New Haven, CT US) (1981)Silva, J.: A survey on algorithmic debugging strategies. Adv. Eng. Softw. 42(11), 976–991 (2011)Silva, J.: A vocabulary of program slicing-based techniques. ACM Comput. Surv. 44(3), 1–12 (2012)Thompson, B., Naish, L.: A guide to the nu-prolog debugging environment. Technical report, University of Melbourne (1997

An Integrated Development Environment for Declarative Multi-Paradigm Programming

In this paper we present CIDER (Curry Integrated Development EnviRonment), an analysis and programming environment for the declarative multi-paradigm language Curry. CIDER is a graphical environment to support the development of Curry programs by providing integrated tools for the analysis and visualization of programs. CIDER is completely implemented in Curry using libraries for GUI programming (based on Tcl/Tk) and meta-programming. An important aspect of our environment is the possible adaptation of the development environment to other declarative source languages (e.g., Prolog or Haskell) and the extensibility w.r.t. new analysis methods. To support the latter feature, the lazy evaluation strategy of the underlying implementation language Curry becomes quite useful.Comment: In A. Kusalik (ed), proceedings of the Eleventh International Workshop on Logic Programming Environments (WLPE'01), December 1, 2001, Paphos, Cyprus. cs.PL/011104

Applying Formal Methods to Networking: Theory, Techniques and Applications

Despite its great importance, modern network infrastructure is remarkable for the lack of rigor in its engineering. The Internet which began as a research experiment was never designed to handle the users and applications it hosts today. The lack of formalization of the Internet architecture meant limited abstractions and modularity, especially for the control and management planes, thus requiring for every new need a new protocol built from scratch. This led to an unwieldy ossified Internet architecture resistant to any attempts at formal verification, and an Internet culture where expediency and pragmatism are favored over formal correctness. Fortunately, recent work in the space of clean slate Internet design---especially, the software defined networking (SDN) paradigm---offers the Internet community another chance to develop the right kind of architecture and abstractions. This has also led to a great resurgence in interest of applying formal methods to specification, verification, and synthesis of networking protocols and applications. In this paper, we present a self-contained tutorial of the formidable amount of work that has been done in formal methods, and present a survey of its applications to networking.Comment: 30 pages, submitted to IEEE Communications Surveys and Tutorial

Towards declarative diagnosis of constraint programs over finite domains

The paper proposes a theoretical approach of the debugging of constraint programs based on a notion of explanation tree. The proposed approach is an attempt to adapt algorithmic debugging to constraint programming. In this theoretical framework for domain reduction, explanations are proof trees explaining value removals. These proof trees are defined by inductive definitions which express the removals of values as consequences of other value removals. Explanations may be considered as the essence of constraint programming. They are a declarative view of the computation trace. The diagnosis consists in locating an error in an explanation rooted by a symptom.Comment: In M. Ronsse, K. De Bosschere (eds), proceedings of the Fifth International Workshop on Automated Debugging (AADEBUG 2003), September 2003, Ghent. cs.SE/030902

Idempotent I/O for safe time travel

Debuggers for logic programming languages have traditionally had a capability most other debuggers did not: the ability to jump back to a previous state of the program, effectively travelling back in time in the history of the computation. This ``retry'' capability is very useful, allowing programmers to examine in detail a part of the computation that they previously stepped over. Unfortunately, it also creates a problem: while the debugger may be able to restore the previous values of variables, it cannot restore the part of the program's state that is affected by I/O operations. If the part of the computation being jumped back over performs I/O, then the program will perform these I/O operations twice, which will result in unwanted effects ranging from the benign (e.g. output appearing twice) to the fatal (e.g. trying to close an already closed file). We present a simple mechanism for ensuring that every I/O action called for by the program is executed at most once, even if the programmer asks the debugger to travel back in time from after the action to before the action. The overhead of this mechanism is low enough and can be controlled well enough to make it practical to use it to debug computations that do significant amounts of I/O.Comment: In M. Ronsse, K. De Bosschere (eds), proceedings of the Fifth International Workshop on Automated Debugging (AADEBUG 2003), September 2003, Ghent. cs.SE/030902