03-30-2016 SWOSU Students Experience Computing Power

Southwestern Oklahoma State University computer science students in Weatherford recently had the opportunity to attend a day-long workshop on High Performance Computing Application Profiling and Debugging

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. 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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. 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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. 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Exploring The Impact Of Cognitive Awareness Scaffolding For Debugging In An Introductory Computer Science Class

Debugging is a significant part of programming. However, a lot of introductory pro- gramming classes tend to focus on writing and reading code than on debugging. They utilize programming assignments that are designed in ways such that students learn debugging by completing these assignments which makes debugging more of an im- plicit goal. In this thesis, we propose a cognitive awareness scaffolding in debugging to help students self-regulate their debugging process. We validate its effectiveness by conducting experiments with students in four sections of a Data Structures course, which is one of the introductory computer science classes at California Polytechnic State University, San Luis Obispo. In this form, students identified the debugging stage, described the bugs in their own words, and tracked their attempts to fix them. The exit survey responses that students filled out at the end of the quarter indi- cate that students seemed to find the debugging form helpful with self-regulation in debugging process. For further investigation, we attempt to measure students’ under- standing of the bugs explained on the form. Additionally, we also discuss potential improvements for the debugging form

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

Turbo Pascal; an introduction to modern programming

The first course in computer science should be one of the highlights of a collage career. Computers are, without doubt, one of the most significant intellectual achievements of our century. A first course in computer science should tell the story of modern computer programming in a setting of drama,excitement, and fun. That’s the purpose of this book. In it, I have attempted to enhance the computer science curriculum with applications from my activities as a working scientist and practical knowledge gained from my activities as a software developer. • The Curriculum And The Course • Pascal Language • Turbo Pascal Environment • Problem Solving and Program Design • Application • Hands On Approach • Testing and Debugging • Teaching and Learning Aid

Electronic prototyping

The potential benefits of automation in space are significant. The science base needed to support this automation not only will help control costs and reduce lead-time in the earth-based design and construction of space stations, but also will advance the nation's capability for computer design, simulation, testing, and debugging of sophisticated objects electronically. Progress in automation will require the ability to electronically represent, reason about, and manipulate objects. Discussed here is the development of representations, languages, editors, and model-driven simulation systems to support electronic prototyping. In particular, it identifies areas where basic research is needed before further progress can be made

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

Users guide for information retrieval using APL

A Programming Language (APL) is a precise, concise, and powerful computer programming language. Several features make APL useful to managers and other potential computer users. APL is interactive; therefore, the user can communicate with his program or data base in near real-time. This, coupled with the fact that APL has excellent debugging features, reduces program checkout time to minutes or hours rather than days or months. Of particular importance is the fact that APL can be utilized as a management science tool using such techniques as operations research, statistical analysis, and forecasting. The gap between the scientist and the manager could be narrowed by showing how APL can be used to do what the scientists and the manager each need to do, retrieve information. Sometimes, the information needs to be retrieved rapidly. In this case APL is ideally suited for this challenge

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

Pirate plunder: game-based computational thinking using scratch blocks

Policy makers worldwide argue that children should be taught how technology works, and that the ‘computational thinking’ skills developed through programming are useful in a wider context. This is causing an increased focus on computer science in primary and secondary education. Block-based programming tools, like Scratch, have become ubiquitous in primary education (5 to 11-years-old) throughout the UK. However, Scratch users often struggle to detect and correct ‘code smells’ (bad programming practices) such as duplicated blocks and large scripts, which can lead to programs that are difficult to understand. These ‘smells’ are caused by a lack of abstraction and decomposition in programs; skills that play a key role in computational thinking. In Scratch, repeats (loops), custom blocks (procedures) and clones (instances) can be used to correct these smells. Yet, custom blocks and clones are rarely taught to children under 11-years-old. We describe the design of a novel educational block-based programming game, Pirate Plunder, which aims to teach these skills to children aged 9-11. Players use Scratch blocks to navigate around a grid, collect items and interact with obstacles. Blocks are explained in ‘tutorials’; the player then completes a series of ‘challenges’ before attempting the next tutorial. A set of Scratch blocks, including repeats, custom blocks and clones, are introduced in a linear difficulty progression. There are two versions of Pirate Plunder; one that uses a debugging-first approach, where the player is given a program that is incomplete or incorrect, and one where each level begins with an empty program. The game design has been developed through iterative playtesting. The observations made during this process have influenced key design decisions such as Scratch integration, difficulty progression and reward system. In future, we will evaluate Pirate Plunder against a traditional Scratch curriculum and compare the debugging-first and non-debugging versions in a series of studies