Automatic Software Repair: a Bibliography

This article presents a survey on automatic software repair. Automatic software repair consists of automatically finding a solution to software bugs without human intervention. This article considers all kinds of repairs. First, it discusses behavioral repair where test suites, contracts, models, and crashing inputs are taken as oracle. Second, it discusses state repair, also known as runtime repair or runtime recovery, with techniques such as checkpoint and restart, reconfiguration, and invariant restoration. The uniqueness of this article is that it spans the research communities that contribute to this body of knowledge: software engineering, dependability, operating systems, programming languages, and security. It provides a novel and structured overview of the diversity of bug oracles and repair operators used in the literature

Dynamic Analysis can be Improved with Automatic Test Suite Refactoring

Context: Developers design test suites to automatically verify that software meets its expected behaviors. Many dynamic analysis techniques are performed on the exploitation of execution traces from test cases. However, in practice, there is only one trace that results from the execution of one manually-written test case. Objective: In this paper, we propose a new technique of test suite refactoring, called B-Refactoring. The idea behind B-Refactoring is to split a test case into small test fragments, which cover a simpler part of the control flow to provide better support for dynamic analysis. Method: For a given dynamic analysis technique, our test suite refactoring approach monitors the execution of test cases and identifies small test cases without loss of the test ability. We apply B-Refactoring to assist two existing analysis tasks: automatic repair of if-statements bugs and automatic analysis of exception contracts. Results: Experimental results show that test suite refactoring can effectively simplify the execution traces of the test suite. Three real-world bugs that could previously not be fixed with the original test suite are fixed after applying B-Refactoring; meanwhile, exception contracts are better verified via applying B-Refactoring to original test suites. Conclusions: We conclude that applying B-Refactoring can effectively improve the purity of test cases. Existing dynamic analysis tasks can be enhanced by test suite refactoring

Squeeziness: An information theoretic measure for avoiding fault masking

Copyright @ 2012 ElsevierFault masking can reduce the effectiveness of a test suite. We propose an information theoretic measure, Squeeziness, as the theoretical basis for avoiding fault masking. We begin by explaining fault masking and the relationship between collisions and fault masking. We then define Squeeziness and demonstrate by experiment that there is a strong correlation between Squeeziness and the likelihood of collisions. We conclude with comments on how Squeeziness could be the foundation for generating test suites that minimise the likelihood of fault masking

The Oracle Problem in Software Testing: A Survey

Testing involves examining the behaviour of a system in order to discover potential faults. Given an input for a system, the challenge of distinguishing the corresponding desired, correct behaviour from potentially incorrect behavior is called the “test oracle problem”. Test oracle automation is important to remove a current bottleneck that inhibits greater overall test automation. Without test oracle automation, the human has to determine whether observed behaviour is correct. The literature on test oracles has introduced techniques for oracle automation, including modelling, specifications, contract-driven development and metamorphic testing. When none of these is completely adequate, the final source of test oracle information remains the human, who may be aware of informal specifications, expectations, norms and domain specific information that provide informal oracle guidance. All forms of test oracles, even the humble human, involve challenges of reducing cost and increasing benefit. This paper provides a comprehensive survey of current approaches to the test oracle problem and an analysis of trends in this important area of software testing research and practice

Achievements, open problems and challenges for search based software testing

Search Based Software Testing (SBST) formulates testing as an optimisation problem, which can be attacked using computational search techniques from the field of Search Based Software Engineering (SBSE). We present an analysis of the SBST research agenda, focusing on the open problems and challenges of testing non-functional properties, in particular a topic we call 'Search Based Energy Testing' (SBET), Multi-objective SBST and SBST for Test Strategy Identification. We conclude with a vision of FIFIVERIFY tools, which would automatically find faults, fix them and verify the fixes. We explain why we think such FIFIVERIFY tools constitute an exciting challenge for the SBSE community that already could be within its reach

Technology readiness levels for machine learning systems

The development and deployment of machine learning (ML) systems can be executed easily with modern tools, but the process is typically rushed and means-to-an-end. The lack of diligence can lead to technical debt, scope creep and misaligned objectives, model misuse and failures, and expensive consequences. Engineering systems, on the other hand, follow well-defined processes and testing standards to streamline development for high-quality, reliable results. The extreme is spacecraft systems, where mission critical measures and robustness are ingrained in the development process. Drawing on experience in both spacecraft engineering and ML (from research through product across domain areas), we have developed a proven systems engineering approach for machine learning development and deployment. Our Machine Learning Technology Readiness Levels (MLTRL) framework defines a principled process to ensure robust, reliable, and responsible systems while being streamlined for ML workflows, including key distinctions from traditional software engineering. Even more, MLTRL defines a lingua franca for people across teams and organizations to work collaboratively on artificial intelligence and machine learning technologies. Here we describe the framework and elucidate it with several real world use-cases of developing ML methods from basic research through productization and deployment, in areas such as medical diagnostics, consumer computer vision, satellite imagery, and particle physics

Technology Readiness Levels for Machine Learning Systems

The development and deployment of machine learning (ML) systems can be executed easily with modern tools, but the process is typically rushed and means-to-an-end. The lack of diligence can lead to technical debt, scope creep and misaligned objectives, model misuse and failures, and expensive consequences. Engineering systems, on the other hand, follow well-defined processes and testing standards to streamline development for high-quality, reliable results. The extreme is spacecraft systems, where mission critical measures and robustness are ingrained in the development process. Drawing on experience in both spacecraft engineering and ML (from research through product across domain areas), we have developed a proven systems engineering approach for machine learning development and deployment. Our "Machine Learning Technology Readiness Levels" (MLTRL) framework defines a principled process to ensure robust, reliable, and responsible systems while being streamlined for ML workflows, including key distinctions from traditional software engineering. Even more, MLTRL defines a lingua franca for people across teams and organizations to work collaboratively on artificial intelligence and machine learning technologies. Here we describe the framework and elucidate it with several real world use-cases of developing ML methods from basic research through productization and deployment, in areas such as medical diagnostics, consumer computer vision, satellite imagery, and particle physics