Spectrum-Based Fault Localization in Model Transformations

Abstract

Model transformations play a cornerstone role in Model-Driven Engineering (MDE), as they provide the essential mechanisms for manipulating and transforming models. The correctness of software built using MDE techniques greatly relies on the correctness of model transformations. However, it is challenging and error prone to debug them, and the situation gets more critical as the size and complexity of model transformations grow, where manual debugging is no longer possible. Spectrum-Based Fault Localization (SBFL) uses the results of test cases and their corresponding code coverage information to estimate the likelihood of each program component (e.g., statements) of being faulty. In this article we present an approach to apply SBFL for locating the faulty rules in model transformations. We evaluate the feasibility and accuracy of the approach by comparing the effectiveness of 18 different state-of-the-art SBFL techniques at locating faults in model transformations. Evaluation results revealed that the best techniques, namely Kulcynski2 , Mountford , Ochiai , and Zoltar , lead the debugger to inspect a maximum of three rules to locate the bug in around 74% of the cases. Furthermore, we compare our approach with a static approach for fault localization in model transformations, observing a clear superiority of the proposed SBFL-based method.

Fixing Multiple Type Errors in Model Transformations with Alternative Oracles to Test Cases.

“…In the second family, for example, Troya et al (Troya et al 2018) presented the Spectrum-Based Fault Localization technique and used the results of test cases to determine the probability of each rule of transformation to be faulty. Similarly Burgueño et al (Burgueño et al 2015) presented a static approach for detecting the faulty rules in model transformations.…”

Fault Localization With Weighted Test Model in Model Transformations

“…Table 3 summarizes this information. Most of them come from the experimental configuration information in the SBFL [11]. Three model transformation artifacts are different in the number of transformation rules, rules size, test cases, OCL assertions, and mutants.…”

“…Aranega et al [9] proposes the trace model on the fault localization, by defining and analyzing a trace model describing the execution information of model transformation to determine a series of transformation rules that cause an error. On the basis of static analysis, Troya et al [11] further propose a spectrum-based technique for improving fault localization effectiveness, collecting the information on model transformation runtime in the passing and failing test models and using the idea of the spectrum to identify the model transformation rules that may be faulty. Similar to static analysis, the spectrum-based approach also provides a list of suspicious rules and experimentally demonstrates that the spectrum-based approach is better than the static analysis.…”

“…In recent years, more researchers begin to study the way of locating faults in model transformations. Current approaches to fault localization include Petri-net analysis [8], trace model [9], static analysis [10], and spectrum-based [11]. Among them, the spectrum-based fault localization (SBFL) [11], is of better accuracy than the static analysis.…”

“…Current approaches to fault localization include Petri-net analysis [8], trace model [9], static analysis [10], and spectrum-based [11]. Among them, the spectrum-based fault localization (SBFL) [11], is of better accuracy than the static analysis. It analyzes the execution coverage information of the rules under the passing and failing test models, applies the statistical analysis approaches [12], [13], and finally calculates the suspicious score for each rule, providing a list of suspicious rules rankings that reflect the most relevant part of the fault.…”