7 research outputs found
Amortising the Cost of Mutation Based Fault Localisation using Statistical Inference
Mutation analysis can effectively capture the dependency between source code
and test results. This has been exploited by Mutation Based Fault Localisation
(MBFL) techniques. However, MBFL techniques suffer from the need to expend the
high cost of mutation analysis after the observation of failures, which may
present a challenge for its practical adoption. We introduce SIMFL (Statistical
Inference for Mutation-based Fault Localisation), an MBFL technique that allows
users to perform the mutation analysis in advance against an earlier version of
the system. SIMFL uses mutants as artificial faults and aims to learn the
failure patterns among test cases against different locations of mutations.
Once a failure is observed, SIMFL requires either almost no or very small
additional cost for analysis, depending on the used inference model. An
empirical evaluation of SIMFL using 355 faults in Defects4J shows that SIMFL
can successfully localise up to 103 faults at the top, and 152 faults within
the top five, on par with state-of-the-art alternatives. The cost of mutation
analysis can be further reduced by mutation sampling: SIMFL retains over 80% of
its localisation accuracy at the top rank when using only 10% of generated
mutants, compared to results obtained without sampling
Spectrum-based Fault Localization Techniques Application on Multiple-Fault Programs: A Review
Software fault localization is one of the most tedious and costly activities in program debugging in the endeavor to identify faults locations in a software program. In this paper, the studies that used spectrum-based fault localization (SBFL) techniques that makes use of different multiple fault localization debugging methods such as one-bug-at-a-time (OBA) debugging, parallel debugging, and simultaneous debugging in localizing multiple faults are classified and critically analyzed in order to extensively discuss the current research trends, issues, and challenges in this field of study. The outcome strongly shows that there is a high utilization of OBA debugging method, poor fault isolation accuracy, and dominant use of artificial faults that limit the existing techniques applicability in the software industry
Graph Structural Residuals: A Learning Approach to Diagnosis
Traditional model-based diagnosis relies on constructing explicit system
models, a process that can be laborious and expertise-demanding. In this paper,
we propose a novel framework that combines concepts of model-based diagnosis
with deep graph structure learning. This data-driven approach leverages data to
learn the system's underlying structure and provide dynamic observations,
represented by two distinct graph adjacency matrices. Our work facilitates a
seamless integration of graph structure learning with model-based diagnosis by
making three main contributions: (i) redefining the constructs of system
representation, observations, and faults (ii) introducing two distinct versions
of a self-supervised graph structure learning model architecture and (iii)
demonstrating the potential of our data-driven diagnostic method through
experiments on a system of coupled oscillators