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

First-order integer programming for MAP problems

Finding the most probable (MAP) model in SRL frameworks such as Markov logic and Problog can, in principle, be solved by encoding the problem as a `grounded-out' mixed integer program (MIP). However, useful first-order structure disappears in this process motivating the development of first-order MIP approaches. Here we present mfoilp, one such approach. Since the syntax and semantics of mfoilp is essentially the same as existing approaches we focus here mainly on implementation and algorithmic issues. We start with the (conceptually) simple problem of using a logic program to generate a MIP instance before considering more ambitious exploitation of first-order representations.Comment: corrected typo

Programming with global analysis

Global data-flow analysis of (constraint) logic programs, which is generally based on abstract interpretation [7], is reaching a comparatively high level of maturity. A natural question is whether it is time for its routine incorporation in standard compilers, something which, beyond a few experimental systems, has not happened to date. Such incorporation arguably makes good sense only if: • the range of applications of global analysis is large enough to justify the additional complication in the compiler, and • global analysis technology can deal with all the features of "practical" languages (e.g., the ISO-Prolog built-ins) and "scales up" for large programs. We present a tutorial overview of a number of concepts and techniques directly related to the issues above, with special emphasis on the first one. In particular, we concéntrate on novel uses of global analysis during program development and debugging, rather than on the more traditional application área of program optimization. The idea of using abstract interpretation for validation and diagnosis has been studied in the context of imperative programming [2] and also of logic programming. The latter work includes issues such as using approximations to reduce the burden posed on programmers by declarative debuggers [6, 3] and automatically generating and checking assertions [4, 5] (which includes the more traditional type checking of strongly typed languages, such as Gódel or Mercury [1, 8, 9]) We also review some solutions for scalability including modular analysis, incremental analysis, and widening. Finally, we discuss solutions for dealing with meta-predicates, side-effects, delay declarations, constraints, dynamic predicates, and other such features which may appear in practical languages. In the discussion we will draw both from the literature and from our experience and that of others in the development and use of the CIAO system analyzer. In order to emphasize the practical aspects of the solutions discussed, the presentation of several concepts will be illustrated by examples run on the CIAO system, which makes extensive use of global analysis and assertions

Finding Minimal Cost Herbrand Models with Branch-Cut-and-Price

Given (1) a set of clauses $T$ in some first-order language $\cal L$ and (2) a cost function $c : B_{{\cal L}} \rightarrow \mathbb{R}_{+}$, mapping each ground atom in the Herbrand base $B_{{\cal L}}$ to a non-negative real, then the problem of finding a minimal cost Herbrand model is to either find a Herbrand model $\cal I$ of $T$ which is guaranteed to minimise the sum of the costs of true ground atoms, or establish that there is no Herbrand model for $T$. A branch-cut-and-price integer programming (IP) approach to solving this problem is presented. Since the number of ground instantiations of clauses and the size of the Herbrand base are both infinite in general, we add the corresponding IP constraints and IP variables `on the fly' via `cutting' and `pricing' respectively. In the special case of a finite Herbrand base we show that adding all IP variables and constraints from the outset can be advantageous, showing that a challenging Markov logic network MAP problem can be solved in this way if encoded appropriately

Concolic Testing in Logic programming

Software testing is one of the most popular validation techniques in the software industry. Surprisingly, we can only find a few approaches to testing in the context of logic programming. In this paper, we introduce a systematic approach for dynamic testing that combines both concrete and symbolic execution. Our approach is fully automatic and guarantees full path coverage when it terminates. We prove some basic properties of our technique and illustrate its practical usefulness through a prototype implementation.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 and by the Generalitat Valenciana under grant PROMETEOII/2015/013. Part of this research was done while the third author was visiting the University of Reunion; G. Vidal gratefully acknowledges their hospitality.Mesnard, F.; Payet, E.; Vidal Oriola, GF. (2015). Concolic Testing in Logic programming. Theory and Practice of Logic Programming. 15(4):711-725. https://doi.org/10.1017/S1471068415000332S711725154SCHIMPF, J., & SHEN, K. (2011). ECLiPSe – From LP to CLP. Theory and Practice of Logic Programming, 12(1-2), 127-156. doi:10.1017/s1471068411000469Martelli, A., & Montanari, U. (1982). An Efficient Unification Algorithm. ACM Transactions on Programming Languages and Systems, 4(2), 258-282. doi:10.1145/357162.357169Godefroid, P., Klarlund, N., & Sen, K. (2005). DART. Proceedings of the 2005 ACM SIGPLAN conference on Programming language design and implementation - PLDI ’05. doi:10.1145/1065010.1065036Mera E. , López-García P. , and Hermenegildo M. V. 2009. Integrating software testing and run-time checking in an assertion verification framework. In 25th International Conference on Logic Programming, ICLP 2009, Pasadena. 281–295.Godefroid, P., Levin, M. Y., & Molnar, D. (2012). SAGE. Communications of the ACM, 55(3), 40. doi:10.1145/2093548.2093564WIELEMAKER, J., SCHRIJVERS, T., TRISKA, M., & LAGER, T. (2011). SWI-Prolog. Theory and Practice of Logic Programming, 12(1-2), 67-96. doi:10.1017/s1471068411000494CARLSSON, M., & MILDNER, P. (2011). SICStus Prolog—The first 25 years. Theory and Practice of Logic Programming, 12(1-2), 35-66. doi:10.1017/s1471068411000482Degrave F. , Schrijvers T. , and Vanhoof W. 2008. Automatic generation of test inputs for Mercury. In Logic-Based Program Synthesis and Transformation, 18th International Symposium, LOPSTR 2008. 71–86.Somogyi, Z., Henderson, F., & Conway, T. (1996). The execution algorithm of mercury, an efficient purely declarative logic programming language. The Journal of Logic Programming, 29(1-3), 17-64. doi:10.1016/s0743-1066(96)00068-4Vidal, G. (2015). Concolic Execution and Test Case Generation in Prolog. Lecture Notes in Computer Science, 167-181. doi:10.1007/978-3-319-17822-6_10Belli F. and Jack O. 1993. Implementation-based analysis and testing of Prolog programs. In ISSTA. 70–80.King, J. C. (1976). Symbolic execution and program testing. Communications of the ACM, 19(7), 385-394. doi:10.1145/360248.360252Lloyd, J. W. (1987). Foundations of Logic Programming. doi:10.1007/978-3-642-83189-8Clarke L. 1976. A program testing system. In Proceedings of the 1976 Annual Conference (ACM'76). 488–491