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

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

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_

Una implementación de Subterm Dependency Tracking para Java

Ortega López, M. (2011). Una implementación de Subterm Dependency Tracking para Java. http://hdl.handle.net/10251/11578.Archivo delegad

University of Melbourne,Australia

ABSTRACT We have implemented a declarative debugger for Mercury that is capable of finding bugs in large, long-running programs. This debugger implements several search strategies. We discuss the implementation of two of these strategies and the conditions under which each strategy is useful. The divide and query strategy tries to minimize the number of questions asked of the user. While divide and query can reduce the number of questions to roughly logarithmic in the size of the computation, implementing it presents practical difficulties for computations whose representations do not fit into memory. We discuss how we get around this problem, making divide and query practical. Our declarative debugger allows users to specify exactly which part of an atom is wrong. The subterm dependency tracking strategy exploits this extra information to jump directly to the part of the program that computed the wrong subterm. In many cases, only a few such jumps are required to arrive at the bug. Subterm dependency tracking can converge on the bug even more quickly than divide and query, and it tends to yield question sequences that are easier for users to answer

Imperative functional programs that explain their work

Program slicing provides explanations that illustrate how program outputs were produced from inputs. We build on an approach introduced in prior work by Perera et al., where dynamic slicing was defined for pure higher-order functional programs as a Galois connection between lattices of partial inputs and partial outputs. We extend this approach to imperative functional programs that combine higher-order programming with references and exceptions. We present proofs of correctness and optimality of our approach and a proof-of-concept implementation and experimental evaluation.Comment: Full version of ICFP 2017 paper, with appendice

An incremental prototyping methodology for distributed systems based on formal specifications

This thesis presents a new incremental prototyping methodology for formally specified distributed systems. The objective of this methodology is to fill the gap which currently exists between the phase where a specification is simulated, generally using some sequential logical inference tool, and the phase where the modeled system has a reliable, efficient and maintainable distributed implementation in a main-stream object-oriented programming language. This objective is realized by application of a methodology we call Mixed Prototyping with Object-Orientation (in short: OOMP). This is an extension of an existing approach, namely Mixed Prototyping, that we have adapted to the object-oriented paradigm, of which we exploit the flexibility and inherent capability of modeling abstract entities. The OOMP process proceeds as follows. First, the source specifications are automatically translated into a class-based object-oriented language, thus providing a portable and high-level initial implementation. The generated class hierarchy is designed so that the developer may independently derive new sub-classes in order to make the prototype more efficient or to add functionalities that could not be specified with the given formalism. This prototyping process is performed incrementally in order to safely validate the modifications against the semantics of the specification. The resulting prototype can finally be considered as the end-user implementation of the specified software. The originality of our approach is that we exploit object-oriented programming techniques in the implementation of formal specifications in order to gain flexibility in the development process. Simultaneously, the object paradigm gives the means to harness this newly acquired freedom by allowing automatic generation of test routines which verify the conformance of the hand-written code with respect to the specifications. We demonstrate the generality of our prototyping scheme by applying it to a distributed collaborative diary program within the frame of CO-OPN (Concurrent Object-Oriented Petri Nets), a very powerful specification formalism which allows expressing concurrent and non-deterministic behaviours, and which provides structuring facilities such as modularity, encapsulation and genericity. An important effort has also been accomplished in the development or adaptation of distributed algorithms for cooperative symbolic resolution. These algorithms are used in the run-time support of the generated CO-OPN prototypes

Programming Languages and Systems

This open access book constitutes the proceedings of the 31st European Symposium on Programming, ESOP 2022, which was held during April 5-7, 2022, in Munich, Germany, as part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2022. The 21 regular papers presented in this volume were carefully reviewed and selected from 64 submissions. They deal with fundamental issues in the specification, design, analysis, and implementation of programming languages and systems

Programming Languages and Systems

This open access book constitutes the proceedings of the 31st European Symposium on Programming, ESOP 2022, which was held during April 5-7, 2022, in Munich, Germany, as part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2022. The 21 regular papers presented in this volume were carefully reviewed and selected from 64 submissions. They deal with fundamental issues in the specification, design, analysis, and implementation of programming languages and systems