Answer Set Programming (ASP) is one of the major declarative programming paradigms in the area of logic programming and non-monotonic reasoning. Despite that ASP features a simple syntax and an intuitive semantics, errors are common during the development of ASP programs. In this paper we propose a novel debugging approach allowing for interactive localization of bugs in non-ground programs. The new approach points the user directly to a set of non-ground rules involved in the bug, which might be refined (up to the point in which the bug is easily identified) by asking the programmer a sequence of questions on an expected answer set. The approach has been implemented on top of the ASP solver WASP. The resulting debugger has been complemented by a user-friendly graphical interface, and integrated in ASPIDE, a rich IDE for answer set programs. In addition, an empirical analysis shows that the new debugger is not affected by the grounding blowup limiting the application of previous approaches based on meta-programming. Under consideration in Theory and Practice of Logic Programming (TPLP).
There are other approaches enabling faults localization in ASP, but not directly comparable with , including Consistency-Restoring Prolog @cite_24 , translation of ASP programs to natural language @cite_12 , visualization of justifications for an answer set @cite_22 as well as stepping through an ASP program @cite_37 . In @cite_2 , the authors present a debugging technique for normal ASP programs that is based on inductive logic programming (ILP) and test cases. The idea is to allow the programmer to specify test cases modeling features that are expected to appear in some solution and those that should not. These are used to to revise the original program semi-automatically so that it satisfies the stated properties. This approach offers the possibility to learn rules (and modifications of rules), whereas focus only on identifying the buggy rules of a given program. Combining these approaches with ideas implemented in is part of our future work.
@cite_37 @cite_22 @cite_24 @cite_2 @cite_12
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When deploying Answer Set Programming ASP in an industrial context, for instance for re-configurationi¾ź[5], knowledge engineers need debugging support on non-ground programs. Current approaches to ASP debugging, however, do not cover extended modeling features of ASP, such as choice rules, conditional literals, cardinality and weight constraintsi¾ź[13]. To this end, we encode non-ground ASP programs using extended modeling features into normal logic progams; this encoding extends existing encodings for the case of ground programs [4,10,11] to the non-ground case. We subsequently deploy this translation on top of an existing ASP debugging approach for non-ground normal logic programsi¾ź[14]. We have implemented and tested the approach and provide evaluation results. Most fault localization techniques take as input a faulty program, and produce as output a ranked list of suspicious code locations at which the program may be defective. When researchers propose a new fault localization technique, they typically evaluate it on programs with known faults. The technique is scored based on where in its output list the defective code appears. This enables the comparison of multiple fault localization techniques to determine which one is better. Previous research has evaluated fault localization techniques using artificial faults, generated either by mutation tools or manually. In other words, previous research has determined which fault localization techniques are best at finding artificial faults. However, it is not known which fault localization techniques are best at finding real faults. It is not obvious that the answer is the same, given previous work showing that artificial faults have both similarities to and differences from real faults. We performed a replication study to evaluate 10 claims in the literature that compared fault localization techniques (from the spectrum-based and mutation-based families). We used 2995 artificial faults in 6 real-world programs. Our results support 7 of the previous claims as statistically significant, but only 3 as having non-negligible effect sizes. Then, we evaluated the same 10 claims, using 310 real faults from the 6 programs. Every previous result was refuted or was statistically and practically insignificant. Our experiments show that artificial faults are not useful for predicting which fault localization techniques perform best on real faults. In light of these results, we identified a design space that includes many previously-studied fault localization techniques as well as hundreds of new techniques. We experimentally determined which factors in the design space are most important, using an overall set of 395 real faults. Then, we extended this design space with new techniques. Several of our novel techniques outperform all existing techniques, notably in terms of ranking defective code in the top-5 or top-10 reports. This paper presents a method for semi-automatic bug localization, generalized algorithmic debugging, which has been integrated with the category partition method for functional testing. In this way the efficiency of the algorithmic debugging method for bug localization can be improved by using test specifications and test results. The long-range goal of this work is a semi-automatic debugging and testing system which can be used during large-scale program development of nontrivial programs. The method is generally applicable to procedural langua ges and is not dependent on any ad hoc assumptions regarding the subject program. The original form of algorithmic debugging, introduced by Shapiro, was however limited to small Prolog programs without side-effects, but has later been generalized to concurrent logic programming languages. Another drawback of the original method is the large number of interactions with the user during bug localization. To our knowledge, this is the first method which uses category partition testing to improve the bug localization properties of algorithmic debugging. The method can avoid irrelevant questions to the programmer by categorizing input parameters and then match these against test cases in the test database. Additionally, we use program slicing, a data flow analysis technique, to dynamically compute which parts of the program are relevant for the search, thus further improving bug localization. We believe that this is the first generalization of algorithmic debugging for programs with side-effects written in imperative languages such as Pascal. These improvements together makes it more feasible to debug larger programs. However, additional improvements are needed to make it handle pointer-related side-effects and concurrent Pascal programs. A prototype generalized algorithmic debugger for a Pascal subset without pointer side-effects and a test case generator for application programs in Pascal, C, dBase, and LOTUS have been implemented. We present a method for performing fault localization using similar program spectra. Our method assumes the existence of a faulty run and a larger number of correct runs. It then selects according to a distance criterion the correct run that most resembles the faulty run, compares the spectra corresponding to these two runs, and produces a report of "suspicious" parts of the program. Our method is widely applicable because it does not require any knowledge of the programinput and no more information from the user than a classification of the runs as either "correct" or "faulty". To experimentally validate the viability of the method, we implemented it in a tool, WHITHER using basic block profiling spectra. We experimented with two different similarity measures and the Siemens suite of 132 programs with injected bugs. To measure the success of the tool, we developed a generic method for establishing the quality of a report. The method is based on the way an "ideal user" would navigate the program using the report to save effort during debugging. The best results we obtained were, on average, above 50 , meaning that our ideal user would avoid looking at half of the program.