Trivial compiler equivalence: A large scale empirical study of a simple, fast and effective equivalent mutant detection technique

Identifying equivalent mutants remains the largest impediment to the widespread uptake of mutation testing. Despite being researched for more than three decades, the problem remains. We propose Trivial Compiler Equivalence (TCE) a technique that exploits the use of readily available compiler technology to address this long-standing challenge. TCE is directly applicable to real-world programs and can imbue existing tools with the ability to detect equivalent mutants and a special form of useless mutants called duplicated mutants. We present a thorough empirical study using 6 large open source programs, several orders of magnitude larger than those used in previous work, and 18 benchmark programs with hand-analysis equivalent mutants. Our results reveal that, on large real-world programs, TCE can discard more than 7% and 21% of all the mutants as being equivalent and duplicated mutants respectively. A human- based equivalence verification reveals that TCE has the ability to detect approximately 30% of all the existing equivalent mutants

Contrasting Mutation Rates from Specific-Locus and Long-Term Mutation-Accumulation Procedures

Until recently, the two predominant ways to estimate mutation rates were the specific-locus method and the mutation-accumulation (Bateman-Mukai) method. Both involve seeding a number of parallel lines from a small, genetically uniform population, growing as long as is feasible but not so long as to allow selection to perturb mutant frequencies, and sometimes using extreme bottlenecks to facilitate the retention of deleterious mutations. In the specific-locus method, mutations are selected according to their specific phenotypes and are confirmed by sequencing. In older versions of the mutation-accumulation method, the increase in variance of a quantitative fitness trait is measured and converted into a mutation rate. More recently, a variation on the mutation-accumulation method has become possible based on phenotype-blind genomic sequencing, which might (or might not) provide improved sampling breadth, usually at the expense of sample size. In a recent study, genomic sequencing was applied to Escherichia coli lines propagated for 40,000 generations and passaged daily via 5,000,000 cells. To mitigate the impact of selection, the only targets employed for rate calculations were putatively neutral synonymous mutations. The mutation rate estimate was about 6-fold lower than obtained previously with a robust specific-locus method. Here I argue that purifying selection acting to shape the strong codon preferences of E. coli is the probable cause of the lower estimate, rather than, for instance, a lower mutation rate in nature than in the laboratory

Biology of BMP signalling and cancer

[Abstract] In recent years, it has been proposed that tumours are not homogeneous but composed of several cellular types like normal tissues. A cellular subtype, which is though to be the origin of tumours as well as their malignant properties (i.e., capacity for regrowth and metastasis), are the cancer stem cells (CSCs). CSCs, like normal stem cells, have a nearly unlimited capacity to self-renew and to proliferate so that are responsible, besides their same auto-perpetuation giving rise to the features previously depicted, also for the generation of the bulk of more differentiated cells in tumour. The altered behaviour of CSCs may be caused by the malfunction of a number of signalling pathways involved in normal embryonic development and in tissue homeostasis in adulthood. Among these signalling pathways are Wnt, Hedgehog, Notch and BMP pathways. In this review, we will focus on the study of molecular aspects of BMP signalling as well as its involvement in cancer

Fitness Uniform Optimization

In evolutionary algorithms, the fitness of a population increases with time by mutating and recombining individuals and by a biased selection of more fit individuals. The right selection pressure is critical in ensuring sufficient optimization progress on the one hand and in preserving genetic diversity to be able to escape from local optima on the other hand. Motivated by a universal similarity relation on the individuals, we propose a new selection scheme, which is uniform in the fitness values. It generates selection pressure toward sparsely populated fitness regions, not necessarily toward higher fitness, as is the case for all other selection schemes. We show analytically on a simple example that the new selection scheme can be much more effective than standard selection schemes. We also propose a new deletion scheme which achieves a similar result via deletion and show how such a scheme preserves genetic diversity more effectively than standard approaches. We compare the performance of the new schemes to tournament selection and random deletion on an artificial deceptive problem and a range of NP-hard problems: traveling salesman, set covering and satisfiability.Comment: 25 double-column pages, 12 figure

A Novel Approach to Mutation Operator Design for MDE Languages

Due to the increasing reliance on the software of systems, such as enterprise systems, a wide array of approaches has been found to facilitate the development of software for such systems. The growth of system complexity, however, has provoked concerns about the quality of the software. One approach that copes with complexity is model driven engineering that uses models containing only essential domain concepts, in order to represent complex systems. With some level of automation, models are then maintained by programs that are prone to error, as they are man-made. In order to find errors in programs, software engineers use mutation testing to build strong test inputs by introducing faults into the tested software using mutation operators. They then study if the introduced faults are detected by the test inputs. There have been few attempts to design mutation operators for model driven languages, which have common metamodeling language models, compared with traditional programming languages. This thesis presents an effective language-agnostic approach for mutation operator design for the rapidly emerging model driven engineering languages by considering metamodeling languages. The approach produces generic operators that can be instantiated to generate concrete ones for a given language model, which can be used to mutate program models that conform to the language model. The approach and generic operators are evaluated using empirical mutation analysis experiments over programs written in the ATL and EOL languages. The results show that the generic operators generated from the approach are instantiatable over ATL and EOL metamodels and have produced low proportions of invalid and equivalent mutants that can impact negatively on any mutation testing process. Also, the generic operators have produced useful mutants such as live and not trivially detected kinds of mutants

Formal mutation testing for Circus

International audienceContext: The demand from industry for more dependable and scalable test-development mechanisms has fostered the use of formal models to guide the generation of tests. Despite many advancements having been obtained with state-based models, such as Finite State Machines (FSMs) and Input/Output Transition Systems (IOTSs), more advanced formalisms are required to specify large, state-rich, concurrent systems. Circus, a state-rich process algebra combining Z, CSP and a refinement calculus, is suitable for this; however, deriving tests from such models is accordingly more challenging. Recently, a testing theory has been stated for Circus, allowing the verification of process refinement based on exhaustive test sets. Objective: We investigate fault-based testing for refinement from Circus specifications using mutation. We seek the benefits of such techniques in test-set quality assertion and fault-based test-case selection. We target results relevant not only for Circus, but to any process algebra for refinement that combines CSP with a data language. Method: We present a formal definition for fault-based test sets, extending the Circus testing theory, and an extensive study of mutation operators for Circus. Using these results, we propose an approach to generate tests to kill mutants. Finally, we explain how prototype tool support can be obtained with the implementation of a mutant generator, a translator from Circus to CSP, and a refinement checker for CSP, and with