Quick fixing ATL model transformations

Cuadrado, Jesús; Guerra, Esther; Lara, Juan de

doi:10.1109/models.2015.7338245

Cited by 7 publications

(14 citation statements)

References 18 publications

(31 reference statements)

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“…This paper extends our previous work [36] with the following contributions: we enlarge our catalogue of quick fixes, including variants and refinements of previously existing ones; we give a detailed account of all of these quick fixes and illustrate them with comprehensive examples; we support speculative analysis; we present a more precise experimental evaluation; we provide a static ranking of fixes which has been determined empirically; we provide a dynamic ranking of fixes created on-demand using speculative analysis; and we compare the dynamic and static rankings.…”

Section: Introductionsupporting

confidence: 89%

“…We have used four error-free transformations for the evaluation: 1) PNML2PetriNet from the Grafcet to PetriNet scenario in the ATL zoo 8 , which we used for an initial evaluation in [36]; 2) an extended and errorfree version of the UML2Intalio transformation used as running example in this paper; 3) Ant2Maven from the ATL zoo; and 4) an extended version of the Class2Table transformation from the ATL zoo. The two latter transformations were chosen because they have few and easily fixable problems and the domains were known for us (i.e., we want to introduce as little bias as possible when fixing the transformations manually).…”

Section: Evaluating Validity and Completenessmentioning

confidence: 99%

“…Table 6 shows the results for the PNML2PetriNet transformation. We present this particular transformation of the evaluation to allow its comparison with the results obtained in [36]. We have increased the number of applicable quick fixes in most cases.…”

Section: Evaluating Validity and Completenessmentioning

confidence: 99%

“…We have added new quick fixes, like Q18.1, which is very useful to correct style problems. Moreover, this evaluation is more robust than the one in [36], since we can reliably track the fixed and newly generated problems (columns Fix and Gen) and test the validity of quick fix applications. Altogether, this new evaluation confirms and generalises the previous one.…”

Section: Evaluating Validity and Completenessmentioning

confidence: 99%

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Quick fixing ATL transformations with speculative analysis

2016

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Model transformations are central components of most model-based software projects. While ensuring their correctness is vital to guarantee the quality of the solution, current transformation tools provide limited support to statically detect and fix errors. In this way, the identification of errors and their correction are nowadays mostly manual activities which incur in high costs. The aim of this work is to improve this situation. Recently, we developed a static analyser that combines program analysis and constraint solving to identify errors in ATL model transformations. In this paper, we present a novel method and system that uses our analyser to propose suitable quick fixes for ATL transformation errors, notably some non-trivial, transformationspecific ones. Our approach supports speculative analysis to help developers select the most appropriate fix by creating a dynamic ranking of fixes, reporting on the consequences of applying a quick fix, and providing a previsualization of each quick fix application. The approach integrates seamlessly with the ATL editor. Moreover, we provide an evaluation based on existing faulty transformations built by a third party, and on automatically generated transformation mutants, which are then corrected with the quick fixes of our catalogue.

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Section: Introductionsupporting

confidence: 89%

Section: Evaluating Validity and Completenessmentioning

confidence: 99%

Section: Evaluating Validity and Completenessmentioning

confidence: 99%

Section: Evaluating Validity and Completenessmentioning

confidence: 99%

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Quick fixing ATL transformations with speculative analysis

2016

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“…Both Muslu et al [46] and Steimann et al [60] also do not rank the repairs as we do in this paper. Cuadrado et al [9] proposed to compute quick fixes for ATL transformations. They also proposed in [10] to detect positive and negative side effects for each quick fix.…”

Section: Related Workmentioning

confidence: 99%

Detecting and exploring side effects when repairing model inconsistencies

Khelladi

Kretschmer²,

Egyed³

2019

Proceedings of the 12th ACM SIGPLAN International Conference on Software Language Engineering

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When software models change, developers often fail in keeping them consistent. Automated support in repairing inconsistencies is widely addressed. Yet, merely enumerating repairs for developers is not enough. A repair can as a side effect cause new unexpected inconsistencies (negative) or even fix other inconsistencies as well (positive). To make matters worse, repairing negative side effects can in turn cause further side effects. Current approaches do not detect and track such side effects in depth, which can increase developers' effort and time spent in repairing inconsistencies. This paper presents an automated approach for detecting and tracking the consequences of repairs, i.e. side effects. It recursively explores in depth positive and negative side effects and identifies paths and cycles of repairs. This paper further ranks repairs based on side effect knowledge so that developers may quickly find the relevant ones. Our approach and its tool implementation have been empirically assessed on 14 case studies from industry, academia, and GitHub. Results show that both positive and negative side effects occur frequently. A comparison with three versioned models showed the usefulness of our ranking strategy based on side effects. It showed that our approach's top prioritized repairs are those that developers would indeed choose. A controlled experiment with 24 participants further highlights the significant influence of side effects and of our ranking of repairs on developers. Developers who received side effect knowledge chose far more repairs with positive side effects and far less with negative side effects, while being 12.3% faster, in contrast to developers who did not receive side effect knowledge. CCS Concepts • Software and its engineering → Model-driven software engineering; Software maintenance tools.

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Recommender systems in model-driven engineering

et al. 2021

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Recommender systems are information filtering systems used in many online applications like music and video broadcasting and e-commerce platforms. They are also increasingly being applied to facilitate software engineering activities. Following this trend, we are witnessing a growing research interest on recommendation approaches that assist with modelling tasks and model-based development processes. In this paper, we report on a systematic mapping review (based on the analysis of 66 papers) that classifies the existing research work on recommender systems for model-driven engineering (MDE). This study aims to serve as a guide for tool builders and researchers in understanding the MDE tasks that might be subject to recommendations, the applicable recommendation techniques and evaluation methods, and the open challenges and opportunities in this field of research.

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Quick fixing ATL model transformations

Cited by 7 publications

References 18 publications

Quick fixing ATL transformations with speculative analysis

Quick fixing ATL transformations with speculative analysis

Detecting and exploring side effects when repairing model inconsistencies

Recommender systems in model-driven engineering

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