An Effective Strategy to Build Up a Balanced Test Suite for Spectrum-Based Fault Localization

During past decades, many automated software faults diagnosis techniques including Spectrum-Based Fault Localization (SBFL) have been proposed to improve the efficiency of software debugging activity. In the field of SBFL, suspiciousness calculation is closely related to the number of failed and passed test cases. Studies have shown that the ratio of the number of failed and passed test case has more significant impact on the accuracy of SBFL than the total number of test cases, and a balanced test suite is more beneficial to improving the accuracy of SBFL. Based on theoretical analysis, we proposed an PNF (Passed test cases, Not execute Faulty statement) strategy to reduce test suite and build up a more balanced one for SBFL, which can be used in regression testing. We evaluated the strategy making experiments using the Siemens program and Space program. Experiments indicated that our PNF strategy can be used to construct a new test suite effectively. Compared with the original test suite, the new one has smaller size (average 90% test case was reduced in experiments) and more balanced ratio of failed test cases to passed test cases, while it has the same statement coverage and fault localization accuracy

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

VERDICTS: Visual Exploratory Requirements Discovery and Injection for Comprehension and Testing of Software

We introduce a methodology and research tools for visual exploratory software analysis. VERDICTS combines exploratory testing, tracing, visualization, dynamic discovery and injection of requirements specifications into a live quick-feedback cycle, without recompilation or restart of the system under test. This supports discovery and verification of software dynamic behavior, software comprehension, testing, and locating the defect origin. At its core, VERDICTS allows dynamic evolution and testing of hypotheses about requirements and behavior, by using contracts as automated component verifiers. We introduce Semantic Mutation Testing as an approach to evaluate concordance of automated verifiers and the functional specifications they represent with respect to existing implementation. Mutation testing has promise, but also has many known issues. In our tests, both black-box and white-box variants of our Semantic Mutation Testing approach performed better than traditional mutation testing as a measure of quality of automated verifiers

On Oracles for Automated Diagnosis and Repair of Software Bugs

This HDR focuses on my work on automatic diagnosis and repair done over the past years. Among my past publications, it highlights three contributions on this topic, respectively published in ACM Transactions on Software Engineering and Methodology (TOSEM), IEEE Transactions on Software Engineering (TSE) and Elsevier Information & Software Technology (IST). My goal is to show that those three contributions share something deep, that they are founded on a unifying concept, which is the one of oracle. The first contribution is about statistical oracles. In the context of object-oriented software, we have defined a notion of context and normality that is specific to a fault class: missing method calls. Those inferred regularities act as oracle and their violations are considered as bugs. The second contribution is about test case based oracles for automatic repair. We describe an automatic repair system that fixes failing test cases by generating a patch. It is founded on the idea of refining the knowledge given by the violation of the oracle of the failing test case into finer-grain information, which we call a “micro-oracle”. By considering micro-oracles, we are capable of obtaining at the same time a precise fault localization diagnostic and a well-formed input-output specification to be used for program synthesis in order to repair a bug. The third contribution discusses a novel generic oracle in the context of exception handling. A generic oracle states properties that hold for many domains. Our technique verifies the compliance to this new oracle using test suite execution and exception injection. This document concludes with a research agenda about the future of engineering ultra-dependable and antifragile software systems

Model-based and Model-free Approaches for Power System Security Assessment

Continuous security assessment of a power system is necessary to insure a reliable, stable, and continuous supply of electrical power to customers. To this end, this dissertation identifies and explores some of the various challenges encountered in the field of power system security assessment. Accordingly, several model-based and/or model-free approaches were developed to overcome these challenges. First, a voltage stability index, named TAVSI, is proposed. This index has three important features: TAVSI applies to general load models including ZIP, exponential, and induction motor loads; TAVSI can be used for both measurement-based and model-based voltage stability assessment; and finally, TAVSI is calculated based on normalized sensitivities which enables identification of weak buses and the definition of a global instability threshold. TAVSI was tested on both the IEEE 14-bus and the 181-bus WECC systems. Results show that TAVSI gives a reliable assessment of system stability. Second, a data-driven and model-based hybrid reinforcement learning approach is proposed for training a control agent to re-dispatch generators’ output power in order to relieve stressed branches. For large power systems, the agent’s action space is highly dimensioned which challenges the successful training of data-driven agents. Therefore, we propose a hybrid approach where model-based actions are utilized to help the agent learn an optimal control policy. The proposed approach was tested and compared to the generic data-driven DDPG-based approach on the IEEE 118-bus system and a larger 2749-bus real-world system. Results show that the hybrid approach performs well for large power systems and that it is superior to the DDPG-based approach. Finally, a Convolutional Neural Network (CNN) based approach is proposed as a faster alternative to the classical AC power flow-based contingency screening. The proposed approach is investigated on both the IEEE 118-bus system and the Texas 2000-bus synthetic system. For such large systems, the implementation of the proposed approach came with several challenges, such as computational burden, learning from imbalanced datasets, and performance evaluation of trained models. Accordingly, this work contributes a set of novel techniques and best practices that enables both efficient and successful implementation of CNN-based multi-contingency classifiers for large power systems

Aerospace medicine and biology: A continuing bibliography with indexes (supplement 287)

This bibliography lists 346 reports, articles and other documents introduced into the NASA scientific and technical information system in July 1986