Rui Abreu

IJCAI Conference 2023 Conference Paper

Augmenting Automated Spectrum Based Fault Localization for Multiple Faults

• Prantik Chatterjee
• Jose Campos
• Rui Abreu
• Subhajit Roy

Spectrum-based Fault Localization (SBFL) uses the coverage of test cases and their outcome (pass/fail) to predict the "suspiciousness'' of program components, e. g. , lines of code. SBFL is, perhaps, the most successful fault localization technique due to its simplicity and scalability. However, SBFL heuristics do not perform well in scenarios where a program may have multiple faulty components. In this work, we propose a new algorithm that "augments'' previously proposed SBFL heuristics to produce a ranked list where faulty components ranked low by base SBFL metrics are ranked significantly higher. We implement our ideas in a tool, ARTEMIS, that attempts to "bubble up'' faulty components which are ranked lower by base SBFL metrics. We compare our technique to the most popular SBFL metrics and demonstrate statistically significant improvement in the developer effort for fault localization with respect to the basic strategies.

EAAI Journal 2022 Journal Article

Exploring Design smells for smell-based defect prediction

• Bruno Sotto-Mayor
• Amir Elmishali
• Meir Kalech
• Rui Abreu

IJCAI Conference 2020 Conference Paper

Diagnosing Software Faults Using Multiverse Analysis

• Prantik Chatterjee
• Abhijit Chatterjee
• Jose Campos
• Rui Abreu
• Subhajit Roy

Spectrum-based Fault Localization (SFL) approaches aim to efficiently localize faulty components from examining program behavior. This is done by collecting the execution patterns of various combinations of components and the corresponding outcomes into a spectrum. Efficient fault localization depends heavily on the quality of the spectra. Previous approaches, including the current state-of-the-art Density- Diversity-Uniqueness (DDU) approach, attempt to generate “good” test-suites by improving certain structural properties of the spectra. In this work, we propose a different approach, Multiverse Analysis, that considers multiple hypothetical universes, each corresponding to a scenario where one of the components is assumed to be faulty, to generate a spectrum that attempts to reduce the expected worst-case wasted effort over all the universes. Our experiments show that the Multiverse Analysis not just improves the efficiency of fault localization but also achieves better coverage and generates smaller test-suites over DDU, the current state-of-the-art technique. On average, our approach reduces the developer effort over DDU by over 16% for more than 92% of the instances. Further, the improvements over DDU are indeed statistically significant on the paired Wilcoxon Signed-rank test.

IJCAI Conference 2019 Conference Paper

Demystifying the Combination of Dynamic Slicing and Spectrum-based Fault Localization

• Sofia Reis
• Rui Abreu
• Marcelo d'Amorim

Several approaches have been proposed to reduce debugging costs through automated software fault diagnosis. Dynamic Slicing (DS) and Spectrum-based Fault Localization (SFL) are popular fault diagnosis techniques and normally seen as complementary. This paper reports on a comprehensive study to reassess the effects of combining DS with SFL. With this combination, components that are often involved in failing but seldom in passing test runs could be located and their suspiciousness reduced. Results show that the DS-SFL combination, coined as Tandem-FL, improves the diagnostic accuracy up to 73. 7% (13. 4% on average). Furthermore, results indicate that the risk of missing faulty statements, which is a DS? s key limitation, is not high? DS misses faulty statements in 9% of the 260 cases. To sum up, we found that the DS-SFL combination was practical and effective and encourage new SFL techniques to be evaluated against that optimization.

IJCAI Conference 2018 Conference Paper

Leveraging Qualitative Reasoning to Improve SFL

• Alexandre Perez
• Rui Abreu

Spectrum-based fault localization (SFL) correlates a system's components with observed failures. By reasoning about coverage, SFL allows for a lightweight way of pinpointing faults. This abstraction comes at the cost of missing certain faults, such as errors of omission, and failing to provide enough contextual information to explain why components are considered suspicious. We propose an approach, named Q-SFL, that leverages qualitative reasoning to augment the information made available to SFL techniques. It qualitatively partitions system components, and treats each qualitative state as a new SFL component to be used when diagnosing. Our empirical evaluation shows that augmenting SFL with qualitative components can improve diagnostic accuracy in 54% of the considered real-world subjects.

IJCAI Conference 2015 Conference Paper

Spectrum-Based Fault Localisation for Multi-Agent Systems

• L
• uacute; cio S. Passos
• Rui Abreu
• Rosaldo J. F. Rossetti

Diagnosing unwanted behaviour in Multi-Agent Systems (MASs) is crucial to ascertain agents’ correct operation. However, generation of MAS models is both error-prone and time intense, as it exponentially increases with the number of agents and their interactions. In this paper, we propose a light-weight, automatic debugging-based technique, coined ESFL-MAS, which shortens the diagnostic process, while only relying on minimal information about the system. ESFL-MAS uses a heuristic that quantifies the suspiciousness of an agent to be faulty; therefore, different heuristics may have different impact on the diagnostic quality. Our experimental evaluation shows that 10 out of 42 heuristics yield the best diagnostic accuracy (96. 26% on average).

AAAI Conference 2013 Conference Paper

A Kernel Density Estimate-Based Approach to Component Goodness Modeling

• Nuno Cardoso
• Rui Abreu

Intermittent fault localization approaches account for the fact that faulty components may fail intermittently by considering a parameter (known as goodness) that quantifies the probability that faulty components may still exhibit correct behavior. Current, state-of-the-art approaches (1) assume that this goodness probability is context independent and (2) do not provide means for integrating past diagnosis experience in the diagnostic mechanism. In this paper, we present a novel approach, coined Non-linear Feedback-based Goodness Estimate (NFGE), that uses kernel density estimations (KDE) to address such limitations. We evaluated the approach with both synthetic and real data, yielding lower estimation errors, thus increasing the diagnosis performance.

AAAI Conference 2011 Conference Paper

Spectrum-Based Sequential Diagnosis

• Alberto Gonzalez-Sanchez
• Rui Abreu
• Hans-Gerhard Gross
• Arjan J. C. van Gemund

We present a spectrum-based, sequential software debugging approach coined SEQUOIA, that greedily selects tests out of a suite of tests to narrow down the set of diagnostic candidates with a minimum number of tests. SEQUOIA handles multiple faults, that can be intermittent, at polynomial time and space complexity, due to a novel, approximate diagnostic entropy estimation approach, which considers the subset of diagnoses that cover almost all Bayesian posterior probability mass. Synthetic experiments show that SEQUOIA achieves much better diagnostic uncertainty reduction compared to random test sequencing. Real programs, taken from the Software Infrastructure Repository, confirm SEQUOIA’s better performance, with a test reduction up to 80% compared to random test sequences.

AIJ Journal 2010 Journal Article

Diagnosing multiple intermittent failures using maximum likelihood estimation

• Rui Abreu
• Arjan J.C. van Gemund

IJCAI Conference 2009 Conference Paper

• Rui Abreu
• Peter Zoeteweij
• Arjan J. C. van Gemund

Logic reasoning approaches to fault diagnosis account for the fact that a component cj may fail intermittently by introducing a parameter gj that expresses the probability the component exhibits correct behavior. This component parameter gj, in conjunction with a priori fault probability, is used in a Bayesian framework to compute the posterior fault candidate probabilities. Usually, information on gj is not known a priori. While proper estimation of gj can have a great impact on the diagnostic accuracy, at present, only approximations have been proposed. We present a novel framework, BARINEL, that computes exact estimations of gj as integral part of the posterior candidate probability computation. BARINEL’s diagnostic performance is evaluated for both synthetic and real software systems. Our results show that our approach is superior to approaches based on classical persistent fault models as well as previously proposed intermittent fault models.