Zoltar

Janssen, Tom; Abreu, Rui; Gemund, Arjan J. C. van

doi:10.1145/1596495.1596502

Cited by 30 publications

(1 citation statement)

References 9 publications

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“…The framework called Barinel [70] combines spectrum-based fault localization and model-based debugging to localize single and multiple bugs in programs. Zoltar [55] is also a tool set for fault localization. Arithmetic Mean, Phi (Geometric Mean), Op2, and Pierce have been considered in some comparative studies with other metrics [78,115,117].…”

Section: Technique Formulamentioning

confidence: 99%

Spectrum-Based Fault Localization in Model Transformations

Troya

Segura

Parejo

et al. 2018

ACM Trans. Softw. Eng. Methodol.

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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.

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Section: Technique Formulamentioning

confidence: 99%

Spectrum-Based Fault Localization in Model Transformations

Troya

Segura

Parejo

et al. 2018

ACM Trans. Softw. Eng. Methodol.

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Framing program comprehension as fault localization

Perez

Abreu

2016

J Software Evolu Process

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Program comprehension is a time-consuming task performed during the process of reusing, reengineering, and enhancing existing systems. There are tools to assist comprehension by means of dynamic analysis, but most cannot identify the topology and the interactions of certain functionality in need of change. We propose an approach, coined Spectrum-based Feature Comprehension (SFC), that borrows techniques from software-fault localization that were proven to be effective even when debugging large applications. SFC analyses the program by exploiting run-time information from test case executions to identify the components that are important for a given feature, helping software engineers to understand how a program is structured and each of the functionality's dependencies are. We present a toolset, coined PANGOLIN, that implements SFC and displays its report to the user using an intuitive visualization. A user study presented demonstrating PANGOLIN's efficiency in locating components that should be inspected when changing a certain functionality. Participants using SFC spent a median of 50 min locating the feature with greater accuracy, whereas participants using coverage tools took 60 min. Finally, we also detail the Participatory Feature Detection approach, where user interactions with the system are captured; removing the hindrance of requiring pre-existing automated tests.

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A Visual DSL for the Certification of Open Source Software

Carção

Martins

2014

Computational Science and Its Applications – ICCSA 2014

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Quality assessment of open source software is becoming an important and active research area. One of the reasons for this recent interest is the consequence of Internet popularity. Nowadays, programming also involves looking for the large set of open source libraries and tools that may be reused when developing our software applications. In order to reuse such open source software artifacts, programmers not only need the guarantee that the reused artifact is certified, but also that independently developed artifacts can be easily combined into a coherent piece of software. In this paper we improve over previous works and describe a visual language that allows programmers to graphically describe how software artifacts can be combined into powerful software certification processes. This paper introduces the visual language and describes how its elements are available to the user through an intuitive interface.

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Zoltar

Cited by 30 publications

References 9 publications

Spectrum-Based Fault Localization in Model Transformations

Spectrum-Based Fault Localization in Model Transformations

Framing program comprehension as fault localization

A Visual DSL for the Certification of Open Source Software

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