Uncertainty-Driven Black-Box Test Data Generation

We can never be certain that a software system is correct simply by testing it, but with every additional successful test we become less uncertain about its correctness. In absence of source code or elaborate specifications and models, tests are usually generated or chosen randomly. However, rather than randomly choosing tests, it would be preferable to choose those tests that decrease our uncertainty about correctness the most. In order to guide test generation, we apply what is referred to in Machine Learning as "Query Strategy Framework": We infer a behavioural model of the system under test and select those tests which the inferred model is "least certain" about. Running these tests on the system under test thus directly targets those parts about which tests so far have failed to inform the model. We provide an implementation that uses a genetic programming engine for model inference in order to enable an uncertainty sampling technique known as "query by committee", and evaluate it on eight subject systems from the Apache Commons Math framework and JodaTime. The results indicate that test generation using uncertainty sampling outperforms conventional and Adaptive Random Testing

Mining State-Based Models from Proof Corpora

Interactive theorem provers have been used extensively to reason about various software/hardware systems and mathematical theorems. The key challenge when using an interactive prover is finding a suitable sequence of proof steps that will lead to a successful proof requires a significant amount of human intervention. This paper presents an automated technique that takes as input examples of successful proofs and infers an Extended Finite State Machine as output. This can in turn be used to generate proofs of new conjectures. Our preliminary experiments show that the inferred models are generally accurate (contain few false-positive sequences) and that representing existing proofs in such a way can be very useful when guiding new ones.Comment: To Appear at Conferences on Intelligent Computer Mathematics 201

Assessing and generating test sets in terms of behavioural adequacy

Identifying a finite test set that adequately captures the essential behaviour of a program such that all faults are identified is a well-established problem. This is traditionally addressed with syntactic adequacy metrics (e.g. branch coverage), but these can be impractical and may be misleading even if they are satisfied. One intuitive notion of adequacy, which has been discussed in theoretical terms over the past three decades, is the idea of behavioural coverage: If it is possible to infer an accurate model of a system from its test executions, then the test set can be deemed to be adequate. Despite its intuitive basis, it has remained almost entirely in the theoretical domain because inferred models have been expected to be exact (generally an infeasible task) and have not allowed for any pragmatic interim measures of adequacy to guide test set generation. This paper presents a practical approach to incorporate behavioural coverage. Our BESTEST approach (1) enables the use of machine learning algorithms to augment standard syntactic testing approaches and (2) shows how search-based testing techniques can be applied to generate test sets with respect to this criterion. An empirical study on a selection of Java units demonstrates that test sets with higher behavioural coverage significantly outperform current baseline test criteria in terms of detected faults

HIF-1α is required for hematopoietic stem cell mobilization and 4-prolyl hydroxylase inhibitors enhance mobilization by stabilizing HIF-1α

Many patients with hematological neoplasms fail to mobilize sufficient numbers of hematopoietic stem cells (HSCs) in response to granulocyte colony-stimulating factor (G-CSF) precluding subsequent autologous HSC transplantation. Plerixafor, a specific antagonist of the chemokine receptor CXCR4, can rescue some but not all patients who failed to mobilize with G-CSF alone. These refractory poor mobilizers cannot currently benefit from autologous transplantation. To discover alternative targetable pathways to enhance HSC mobilization, we studied the role of hypoxia-inducible factor-1α (HIF-1α) and the effect of HIF-1α pharmacological stabilization on HSC mobilization in mice. We demonstrate in mice with HSC-specific conditional deletion of the Hif1a gene that the oxygen-labile transcription factor HIF-1α is essential for HSC mobilization in response to G-CSF and Plerixafor. Conversely, pharmacological stabilization of HIF-1α with the 4-prolyl hydroxylase inhibitor FG-4497 synergizes with G-CSF and Plerixafor increasing mobilization of reconstituting HSCs 20-fold compared with G-CSF plus Plerixafor, currently the most potent mobilizing combination used in the clinic

Active inference of EFSMs without reset

Extended finite state machines (EFSMs) model stateful systems with internal data variables, and have many software engineering applications, including system analysis and test case generation. Where such models are not available, it is desirable to reverse engineer them by observing system behaviour, but existing approaches are either limited to classical FSM models with no internal data state, or implicitly require the ability to reset the system under inference, which may not always be possible. In this paper, we present an extension to the hW-inference algorithm that can infer EFSM models, complete with guards and internal data update functions, from systems without a reliable reset, although there are currently some restrictions on the type of system and model

Structural and kinetic characterisation of Trypanosoma congolense pyruvate kinase

Trypanosoma are blood-borne parasites and are the causative agents of neglected tropical diseases (NTDs) affecting both humans and animals. These parasites mainly rely on glycolysis for their energy production within the mammalian host, which is why trypanosomal glycolytic enzymes have been pursued as interesting targets for the development of trypanocidal drugs. The structure-function relationships of pyruvate kinases (PYKs) from trypanosomatids (Trypanosoma and Leishmania) have been well-studied within this context. In this paper, we describe the structural and enzymatic characterization of PYK from T. congolense (TcoPYK), the main causative agent of Animal African Trypanosomosis (AAT), by employing a combination of enzymatic assays, thermal unfolding studies and X-ray crystallography

Dynamic design: manipulation of millisecond timescale motions on the energy landscape of cyclophilin A

Proteins need to interconvert between many conformations in order to function, many of which are formed transiently, and sparsely populated. Particularly when the lifetimes of these states approach the millisecond timescale, identifying the relevant structures and the mechanism by which they interconvert remains a tremendous challenge. Here we introduce a novel combination of accelerated MD (aMD) simulations and Markov state modelling (MSM) to explore these ‘excited’ conformational states. Applying this to the highly dynamic protein CypA, a protein involved in immune response and associated with HIV infection, we identify five principally populated conformational states and the atomistic mechanism by which they interconvert. A rational design strategy predicted that the mutant D66A should stabilise the minor conformations and substantially alter the dynamics, whereas the similar mutant H70A should leave the landscape broadly unchanged. These predictions are confirmed using CPMG and R1ρ solution state NMR measurements. By efficiently exploring functionally relevant, but sparsely populated conformations with millisecond lifetimes in silico, our aMD/MSM method has tremendous promise for the design of dynamic protein free energy landscapes for both protein engineering and drug discovery

Learning EFSM models with registers in guards.

This paper presents an active inference method for Extended Finite State Machines, where inputs and outputs are parametrized, and transitions can be conditioned by guards involving input parameters and internal variables called registers. The method applies to (software) systems that cannot be reset, so it learns an EFSM model of the system on a single trace