Clinical and molecular characterization of a cardiac ryanodine receptor founder mutation causing catecholaminergic polymorphic ventricular tachycardia

Background Catecholaminergic polymorphic ventricular tachycardia (CPVT) is a difficult-to-diagnose cause of sudden cardiac death (SCD). We identified a family of 1400 individuals with multiple cases of CPVT, including 36 SCDs during youth. Objectives We sought to identify the genetic cause of CPVT in this family, to preventively treat and clinically characterize the mutation-positive individuals, and to functionally characterize the pathogenic mechanisms of the mutation. Methods Genetic testing was performed for 1404 relatives. Mutation-positive individuals were preventively treated with β-blockers and clinically characterized with a serial exercise treadmill test (ETT) and Holter monitoring. In vitro functional studies included caffeine sensitivity and store overload–induced calcium release activity of the mutant channel in HEK293 cells. Results We identified the p.G357S_RyR2 mutation, in the cardiac ryanodine receptor, in 179 family members and in 6 SCD cases. No SCD was observed among treated mutation-positive individuals over a median follow-up of 37 months; however, 3 relatives who had refused genetic testing (confirmed mutation-positive individuals) experienced SCD. Holter monitoring did not provide relevant information for CPVT diagnosis. One single ETT was unable to detect complex cardiac arrhythmias in 72% of mutation-positive individuals, though the serial ETT improved the accuracy. Functional studies showed that the G357S mutation increased caffeine sensitivity and store overload–induced calcium release activity under conditions that mimic catecholaminergic stress. Conclusion Our study supports the use of genetic testing to identify individuals at risk of SCD to undertake prophylactic interventions. We also show that the pathogenic mechanisms of p.G357S_RyR2 appear to depend on β-adrenergic stimulation

Dynamic Mutant Subsumption Analysis using LittleDarwin

Many academic studies in the field of software testing rely on mutation testing to use as their comparison criteria. However, recent studies have shown that redundant mutants have a significant effect on the accuracy of their results. One solution to this problem is to use mutant subsumption to detect redundant mutants. Therefore, in order to facilitate research in this field, a mutation testing tool that is capable of detecting redundant mutants is needed. In this paper, we describe how we improved our tool, LittleDarwin, to fulfill this requirement

Semantic mutation testing

This is the Pre-print version of the Article. The official published version can be obtained from the link below - Copyright @ 2011 ElsevierMutation testing is a powerful and flexible test technique. Traditional mutation testing makes a small change to the syntax of a description (usually a program) in order to create a mutant. A test suite is considered to be good if it distinguishes between the original description and all of the (functionally non-equivalent) mutants. These mutants can be seen as representing potential small slips and thus mutation testing aims to produce a test suite that is good at finding such slips. It has also been argued that a test suite that finds such small changes is likely to find larger changes. This paper describes a new approach to mutation testing, called semantic mutation testing. Rather than mutate the description, semantic mutation testing mutates the semantics of the language in which the description is written. The mutations of the semantics of the language represent possible misunderstandings of the description language and thus capture a different class of faults. Since the likely misunderstandings are highly context dependent, this context should be used to determine which semantic mutants should be produced. The approach is illustrated through examples with statecharts and C code. The paper also describes a semantic mutation testing tool for C and the results of experiments that investigated the nature of some semantic mutation operators for C

Mutation testing from probabilistic finite state machines

Mutation testing traditionally involves mutating a program in order to produce a set of mutants and using these mutants in order to either estimate the effectiveness of a test suite or to drive test generation. Recently, however, this approach has been applied to specifications such as those written as finite state machines. This paper extends mutation testing to finite state machine models in which transitions have associated probabilities. The paper describes several ways of mutating a probabilistic finite state machine (PFSM) and shows how test sequences that distinguish between a PFSM and its mutants can be generated. Testing then involves applying each test sequence multiple times, observing the resultant output sequences and using results from statistical sampling theory in order to compare the observed frequency of each output sequence with that expected

Is the Stack Distance Between Test Case and Method Correlated With Test Effectiveness?

Mutation testing is a means to assess the effectiveness of a test suite and its outcome is considered more meaningful than code coverage metrics. However, despite several optimizations, mutation testing requires a significant computational effort and has not been widely adopted in industry. Therefore, we study in this paper whether test effectiveness can be approximated using a more light-weight approach. We hypothesize that a test case is more likely to detect faults in methods that are close to the test case on the call stack than in methods that the test case accesses indirectly through many other methods. Based on this hypothesis, we propose the minimal stack distance between test case and method as a new test measure, which expresses how close any test case comes to a given method, and study its correlation with test effectiveness. We conducted an empirical study with 21 open-source projects, which comprise in total 1.8 million LOC, and show that a correlation exists between stack distance and test effectiveness. The correlation reaches a strength up to 0.58. We further show that a classifier using the minimal stack distance along with additional easily computable measures can predict the mutation testing result of a method with 92.9% precision and 93.4% recall. Hence, such a classifier can be taken into consideration as a light-weight alternative to mutation testing or as a preceding, less costly step to that.Comment: EASE 201

BRAF V600E mutations in urine and plasma cell-free DNA from patients with Erdheim-Chester disease.

Erdheim-Chester disease (ECD) is a rare histiocytosis with a high prevalence of BRAF V600E mutation (>50% of patients). Patients with BRAF-mutant ECD can respond to BRAF inhibitors. Unfortunately, the lack of adequate archival tissue often precludes BRAF testing. We hypothesized that cell-free DNA (cfDNA) from plasma or urine can offer an alternative source of biologic material for testing. We tested for BRAF V600E mutation in cfDNA from the plasma and urine of 6 ECD patients. In patients with available archival tissue, the result of BRAF mutation analysis was concordant with plasma and urine cfDNA results in all 3 patients (100% agreement, kappa 1.00). In all 6 patients, BRAF mutation analysis of plasma and urine cfDNA was concordant in 5 of 6 patients (83% agreement, kappa 0.67). Testing for BRAF V600E mutation in plasma and urine cfDNA should be further investigated as an alternative to archival tissue mutation analysis

LittleDarwin: a Feature-Rich and Extensible Mutation Testing Framework for Large and Complex Java Systems

Mutation testing is a well-studied method for increasing the quality of a test suite. We designed LittleDarwin as a mutation testing framework able to cope with large and complex Java software systems, while still being easily extensible with new experimental components. LittleDarwin addresses two existing problems in the domain of mutation testing: having a tool able to work within an industrial setting, and yet, be open to extension for cutting edge techniques provided by academia. LittleDarwin already offers higher-order mutation, null type mutants, mutant sampling, manual mutation, and mutant subsumption analysis. There is no tool today available with all these features that is able to work with typical industrial software systems.Comment: Pre-proceedings of the 7th IPM International Conference on Fundamentals of Software Engineerin