Causal Inference Based Fault Localization For Python Numerical Programs
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Causal Inference Based Fault Localization For Python Numerical Programs
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Author : Jiacheng Ding
language : en
Publisher:
Release Date : 2018
Causal Inference Based Fault Localization For Python Numerical Programs written by Jiacheng Ding and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with Computer engineering categories.
Fault localization is among the most time-consuming processes in software development, faults in numerical programs are difficult to identify. To address this problem, we propose a causal inference based fault localization technique that statistically estimates the causal effect of numerical variables on programs failure occurrence. This work consists of two parts. First, a Static Single Assignment (SSA) form converter is developed to instrument Python source code and facilitate value profiling. Second, we use Random Forest to estimate potential outcomes of multi-level treatment and search the maximum difference to assign program variables suspiciousness scores. In the experiment, we test how our method reacts to various types of faults and show an empirical result that our method outperforms an existed similar method-ESP.
Value Based Fault Localization In Java Numerical Software With Causal Inference Technique
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Author : Jian Sheng
language : en
Publisher:
Release Date : 2019
Value Based Fault Localization In Java Numerical Software With Causal Inference Technique written by Jian Sheng and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with Fault-tolerant computing categories.
We present a new value-based causal inference model for fault localization in numerical software. The new model utilizes the feature of Gated Single Assignment (GSA) to convert and instrument the Java source code. Then we make use of causal statistical analyses to locate the faulty expression. To estimate the average probability of each variable for causing the fault, we use standardization to mitigating the confounding bias and random forest to compute the average outcome under many treatment representatives. We apply our new model to three Java numerical libraries, and generate faulty subject programs together with test cases for evaluation.
Localizing Faults In Numerical Software Using A Value Based Causal Model
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Author : Zhuofu Bai
language : en
Publisher:
Release Date : 2015
Localizing Faults In Numerical Software Using A Value Based Causal Model written by Zhuofu Bai and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015 with Computer science categories.
Abstract: Causal statistical fault localization (CSFL) techniques, which applies causal inference techniques to test-execution profiles and test outcomes, estimates the causal effect of individual program elements on the occurrence of failures, and have been found to be effective in localizing software faults. In most research on CSFL, execution dynamics have been characterized by code-coverage profiles that indicate which statements, branches, or paths were covered by an execution. This coverage-based CSFL has two potential problems: (1) causal effect estimation can be biased when an important precondition for causal inference, called positivity, is violated (2) it is poorly suited for localizing faults in numerical programs and subprograms having relatively few conditional branches. To solve the aforementioned problems, we first investigate the performance of Baah et al’s causal regression model for fault localization when the positivity condition, for all t, x Pr[T=t|X=x]>0 is violated, where T is a coverage indicator for the target statement and X is the set of covariates used for confounding adjustment. Two kinds of positivity violations are considered: structural and random ones. We prove that random, but not structural nonpositivity may harm the performance of Baah et al’s causal estimator. To address the problem of random nonpositivity, we propose a modification to the way suspiciousness scores are assigned. Empirical results are presented that indicate it improves the performance of Baah et al’s technique. We also present a probabilistic characterization of Baah et al’s estimator, which provides a more efficient way to compute it. Then we present two value-based causal inference models for localizing faults in numerical software. The first model is denoted NUMFL. NUMFL combines causal and statistical analyses to characterize the causal effects of individual numerical expressions on output errors. Given value-profiles for an expression's variables, NUMFL uses generalized propensity scores (GPSs) or covariate balancing propensity scores (CBPSs) to reduce confounding bias caused by confounding variables, which are the evaluation of other, faulty expressions. It estimates the average failure-causing effect (AFCE) of an expression using quadratic regression models fit within GPS or CBPS subclasses. We report on an empirical evaluation of NUMFL involving components from four Java numerical libraries, in which it was compared to five alternative statistical fault localization metrics. The results indicate that NUMFL is more effective than baseline techniques. We also found that NUMFL works fairly well with data from failing runs alone. The second model is based on Bayesian Additive Regression Trees (BART), which are due to Chipman et al. Instead of controlling confounding bias with propensity scores, we use a BART model to approximate the dose-response function (DRF) relating the treatment variable to the output errors, where treatment variable is the result of evaluating a numerical expression. Given a unit in the observational data set, we input it into the fitted BART model, and then increase the value of the treatment variable, but keep the confounding variables unchanged. The causal effect of treatment for that unit is estimated by the change in the output of the BART model. The average value of the estimated causal effect of each unit in the data set is the estimated AFCE of the expression. We compare the performance of BART model with that of NUMFL and five baseline techniques in an empirical evaluation. The result shows BART model is the most effect techniques overall.
Statistical Causal Analysis For Fault Localization
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Author : George Kofi Baah
language : en
Publisher:
Release Date : 2012
Statistical Causal Analysis For Fault Localization written by George Kofi Baah and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012 with Computer software categories.
The ubiquitous nature of software demands that software is released without faults. However, software developers inadvertently introduce faults into software during development. To remove the faults in software, one of the tasks developers perform is debugging. However, debugging is a difficult, tedious, and time-consuming process. Several semi-automated techniques have been developed to reduce the burden on the developer during debugging. These techniques consist of experimental, statistical, and program-structure based techniques. Most of the debugging techniques address the part of the debugging process that relates to finding the location of the fault, which is referred to as fault localization. The current fault-localization techniques have several limitations. Some of the limitations of the techniques include (1) problems with program semantics, (2) the requirement for automated oracles, which in practice are difficult if not impossible to develop, and (3) the lack of theoretical basis for addressing the fault-localization problem. \r : \r : The thesis of this dissertation is that statistical causal analysis combined with program analysis is a feasible and effective approach to finding the causes of software failures. The overall goal of this research is to significantly extend the state of the art in fault localization. To extend the state-of-the-art, a novel probabilistic model that combines program-analysis information with statistical information in a principled manner is developed. The model known as the probabilistic program dependence graph (PPDG) is applied to the fault-localization problem. The insights gained from applying the PPDG to fault localization fuels the development of a novel theoretical framework for fault localization based on established causal inference methodology. The development of the framework enables current statistical fault-localization metrics to be analyzed from a causal perspective. The analysis of the metrics show that the metrics are related to each other thereby allowing the unification of the metrics. Also, the analysis of metrics from a causal perspective reveal that the current statistical techniques do not find the causes of program failures instead the techniques find the program elements most associated with failures. However, the fault-localization problem is a causal problem and statistical association does not imply causation. Several empirical studies are conducted on several software subjects and the results (1) confirm our analytical results, (2) demonstrate the efficacy of our causal technique for fault localization. The results demonstrate the research in this dissertation significantly improves on the state-of-the-art in fault localization.
Statistical Fault Localization And Causal Interactions
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Author : Shih-Feng Sun
language : en
Publisher:
Release Date : 2017
Statistical Fault Localization And Causal Interactions written by Shih-Feng Sun and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017 with Computer science categories.
Coverage-based statistical fault-localization (CBSFL) techniques have been proposed for characterizing the "suspiciousness" of program statements based on code-coverage profiles and PASS/FAIL labels for a set of test or operational executions. The resulting suspiciousness scores are typically intended to be used to rank statements for inspection by developers, on the assumption that statements which cause labeled failures will tend to receive high ranks. Many CBSFL metrics have been proposed. This dissertation examines two issues related to proposed CBSFL metrics. First, it examines the structure of some of the most effective of the proposed metrics, when they are expressed as expressions involving relatively simple probabilities. It is shown that these metrics have a common structure that makes it easier to understand their strengths and weaknesses. The second issue examined here is the impact, on the occurrence of observable program failures, of dynamic interactions between the execution of one program statement and the execution of another. To clarify this issue, event-based definitions of fault-revealing and fault-concealing interactions (FRIs and FCIs) in programs are presented and they are then related to both the theory of causal interactions developed by causal inference researchers and to characterizations of fault-interactions and failed error propagation developed by software researchers. It is shown that statistical tests developed to detect causal interactions are applicable to detecting FRIs and FCIs. A preliminary approach, based on causal interaction tests, is proposed for locating statements involved in both FRIs and FCIs.
Handbook Of Software Fault Localization
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Author : W. Eric Wong
language : en
Publisher: John Wiley & Sons
Release Date : 2023-04-21
Handbook Of Software Fault Localization written by W. Eric Wong and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-04-21 with Computers categories.
Handbook of Software Fault Localization A comprehensive analysis of fault localization techniques and strategies In Handbook of Software Fault Localization: Foundations and Advances, distinguished computer scientists Prof. W. Eric Wong and Prof. T.H. Tse deliver a robust treatment of up-to-date techniques, tools, and essential issues in software fault localization. The authors offer collective discussions of fault localization strategies with an emphasis on the most important features of each approach. The book also explores critical aspects of software fault localization, like multiple bugs, successful and failed test cases, coincidental correctness, faults introduced by missing code, the combination of several fault localization techniques, ties within fault localization rankings, concurrency bugs, spreadsheet fault localization, and theoretical studies on fault localization. Readers will benefit from the authors’ straightforward discussions of how to apply cost-effective techniques to a variety of specific environments common in the real world. They will also enjoy the in-depth explorations of recent research directions on this topic. Handbook of Software Fault Localization also includes: A thorough introduction to the concepts of software testing and debugging, their importance, typical challenges, and the consequences of poor efforts Comprehensive explorations of traditional fault localization techniques, including program logging, assertions, and breakpoints Practical discussions of slicing-based, program spectrum-based, and statistics-based techniques In-depth examinations of machine learning-, data mining-, and model-based techniques for software fault localization Perfect for researchers, professors, and students studying and working in the field, Handbook of Software Fault Localization: Foundations and Advances is also an indispensable resource for software engineers, managers, and software project decision makers responsible for schedule and budget control.
Ionic
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Author : Nadeegi Himahansi Fernando Dombawalage
language : en
Publisher:
Release Date : 2019
Ionic written by Nadeegi Himahansi Fernando Dombawalage and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with categories.
Fault localization techniques-FLTs assist in efficient localization of faults in software programs. However, conventional FLTs such as heuristic-based Spectrum Based Fault Localization are mainly investigated on the individual statement granularity. At statement granularity, elements are considered in isolation. Hence, the information flow between them is ignored. This creates unfavourable circumstances when localizing defects with multiple collaborating faulty program elements. This thesis presents Ionic, an extension to an existing statement level FLT that incorporates information about how statements may collaborate to cause a bug. Ionic leverages the construction of causal models for collections of statements with respect to a bug. Ionic is then used within a new FLT, Hera, to efficiently localize bugs including multiple collaborating statements. Experimental results on real world software defects in the Defects4J benchmark suite show that Hera is better at localizing defects with multiple collaborating faulty elements.
Essential Spectrum Based Fault Localization
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Author : Xiaoyuan Xie
language : en
Publisher: Springer Nature
Release Date : 2021-02-04
Essential Spectrum Based Fault Localization written by Xiaoyuan Xie and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-02-04 with Computers categories.
Program debugging has always been a difficult and time-consuming task in the context of software development, where spectrum-based fault localization (SBFL) is one of the most widely studied families of techniques. While it’s not particularly difficult to learn about the process and empirical performance of a particular SBFL technique from the available literature, researchers and practitioners aren’t always familiar with the underlying theories. This book provides the first comprehensive guide to fundamental theories in SBFL, while also addressing some emerging challenges in this area. The theoretical framework introduced here reveals the intrinsic relations between various risk evaluation formulas, making it possible to construct a formula performance hierarchy. Further extensions of the framework provide a sufficient and necessary condition for a general maximal formula, as well as performance comparisons for hybrid SBFL methods. With regard to emerging challenges in SBFL, the book mainly covers the frequently encountered oracle problem in SBFL and introduces a metamorphic slice-based solution. In addition, it discusses the challenge of multiple-fault localization and presents cutting-edge approaches to overcoming it. SBFL is a widely studied research area with a massive amount of publications. Thus, it is essential that the software engineering community, especially those involved in program debugging, software maintenance and software quality assurance (including both newcomers and researchers who want to gain deeper insights) understand the most fundamental theories – which could also be very helpful to ensuring the healthy development of the field.
Data Driven Methods For Fault Localization In Process Technology
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Author : Kuehnert, Christian
language : en
Publisher: KIT Scientific Publishing
Release Date : 2013-10-24
Data Driven Methods For Fault Localization In Process Technology written by Kuehnert, Christian and has been published by KIT Scientific Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-10-24 with Mathematics categories.
Control systems at production plants consist of a large number of process variables. When detecting abnormal behavior, these variables generate an alarm. Due to the interconnection of the plant's devices the fault can lead to an alarm flood. This again hides the original location of the causing device. In this work several data-driven approaches for root cause localization are proposed, compared and combined. All methods analyze disturbed process data for backtracking the propagation path.
Causal Inference And Discovery In Python
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Author : Aleksander Molak
language : en
Publisher:
Release Date : 2023
Causal Inference And Discovery In Python written by Aleksander Molak and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023 with categories.