Search-based Similarity-driven Behavioural SPL Testing

International audienceDissimilar test cases have been proven to be effective to reveal faults in software systems. In the Software Product Line (SPL) context, this criterion has been applied successfully to mimic combinatorial interaction testing in an efficient and scalable manner by selecting and prioritising most dissimilar configurations of feature models using evolutionary algorithms. In this paper, we extend dissimilarity to behavioural SPL models (FTS) in a search-based approach, and evaluate its effectiveness in terms of product and fault coverage. We investigate different distances as well as as single-objective algorithms, (dissimilarity on actions, random , all-actions). Our results on four case studies show the relevance of dissimilarity-based test generation for be-havioural SPL models, especially on the largest case-study where no other approach can match it

How the human brain introspects about one's own episodes of cognitive control

Available online 8 November 2017.Metacognition refers to our capacity to reflect upon our experiences, thoughts and actions. Metacognition processes are linked to cognitive control functions that allow keeping our actions on-task. But it is unclear how the human brain builds an internal model of one's cognition and behaviour. We conducted two functional magnetic resonance imaging (fMRI) experiments in which brain activity was recorded ‘online’ as participants engaged in a memory-guided search task and then later ‘offline’ when participants introspected about their prior experience and cognitive states during performance. In Experiment 1 the memory cues were task-relevant while in Experiment 2 they were irrelevant. Across Experiments, the patterns of brain activity, including frontoparietal regions, were similar during on-task and introspection states. However the connectivity profile amongst frontoparietal areas was distinct during introspection and modulated by the relevance of the memory cues. Introspection was also characterized by increased temporal correlation between the default-mode network (DMN), frontoparietal and dorsal attention networks and visual cortex. We suggest that memories of one's own experience during task performance are encoded in large-scale patterns of brain activity and that coupling between DMN and frontoparietal control networks may be crucial to build an internal model of one's behavioural performance.D.S. acknowledges support from the Spanish Ministry of Economy and Competitiveness (MINECO), through the ’Severo Ochoa’ Programme for Centres/Units of Excellence in R&D (SEV-2015-490), and project grant PSI2016-76443-P which is also funded by the Agencia Estatal de Investigacion (AEI) and Fondo Europeo de Desarrollo Regional (FEDER)

Cost-effective model-based test case generation and prioritization for software product line

In Software Product Line (SPL), testing is used to manage core assets that comprised variability and commonality in effective ways due to large sizes of products that continue to be developed. SPL testing requires a technique that is capable to manage SPL core assets. Model-based Testing (MBT) is a promising technique that offers automation and reusability in test cases generation. However, there are difficulties to ensure testing in MBT can achieve good test cases generation results based on cost (size of test suite, total execution time) and effectiveness (coverage criteria, fault detection rate) measures. This is due to lack of trade-off between cost and effectiveness in test cases generated in MBT for SPL. This study aims to increase quality of test cases based on cost and effectiveness by using generation and prioritization approaches for MBT in SPL. This study focuses on three parts to enhance quality of test cases. First, test model development based on traceability link. In order to improve test cases quality, this study focused on implementation of hybrid-based and hyper-heuristic based techniques to generate test cases. This is followed by Test Cases Prioritization (TCP) technique that is based on dissimilarity-based technique with string distance. These test cases generation and prioritization approaches are evaluated by using two benchmarks - one test object and one real object. The results are compared with other prominent approaches. The mapping approach showed 10.27% and 32.39% f-measure improvement against existing approach on e-shop object, respectively. For test cases generation using hybrid-based approach, the proposed approach outperformed existing approaches with 11.66% coverage, 17.78% average execution time, and 45.98% average size of test suite on vending machine object. The hyper-heuristic based approach NSGA-II-LHH outperformed other proposed low-level heuristic approaches with 12.00% improvement on coverage, 46.66% average execution time and 42.54% average size of test suite. Furthermore, evaluation of TCP approaches showed fault detection improvement of 21.60%, 10.40% and 12.20% and total execution time improvement of 48.00%, 22.70% and 31.80% in comparison with three existing approaches. The results revealed that proposed model transformations, test cases generation and prioritization approaches significantly improve cost and effectiveness measure in MBT for SPL

The interplay between selective attention and working memory: A behavioural, neural and computational perspective

Selective attention (SA), the process by which information is prioritized for processing according to its relevance to current goals, and working memory (WM), the temporary storage and/or manipulation of information in mind, are considered to be important building blocks in human cognition. Both are essential for coordinating thought and action, and both are foundational for the emergence of other more complex executive functions, like planning and problem solving. The complicated interplay between SA and WM has been investigated across a growing number of experimental studies, with attentional processes influencing various stages of WM, and vice versa. Behavioural evidence suggests that SA can bias processing of information as we anticipate, encode and maintain contents in memory, whilst WM can serve to maintain a template as we search. Neuroimaging studies have observed a highly similar frontoparietal network subserving both processes, indicating anatomical and functional overlap in their corresponding neural mechanisms. Nevertheless, despite substantial evidence for cognitive and neural overlap, almost everything we know about the relationship between SA and WM is derived using group-average performance. In reality, some individuals may rely on shared sub-processes to perform tasks more so than others. In this thesis we extended previous work by understanding this individual variability. The first experimental chapter describes the development of two behavioural paradigms tapping SA and WM. These paradigms are better suited to address this question, relative to previous experimental approaches, because they are matched on task-specific features while being independently scalable in terms of difficulty. The second experimental chapter used functional magnetic resonance imaging (fMRI) in combination with these tasks to identify the neural correlates of individual differences in the strength of SA-WM coupling across participants. The third experimental chapter builds upon the neuroimaging study and addresses whether computational models trained to perform the same set of tasks share any mechanistic properties observed in the human brain, providing a useful framework in which predictions about the relationship between cognitive processes can be readily tested. Lastly, in the final experiment we used cognitive training to test whether altering SA would lead to changes in the related WM system, and whether these gains are modulated by baseline individual differences in the strength of their coupling. Together, along with an opening General Introduction and concluding Discussion, these chapters explore heterogeneity in the relationship between SA and WM from multiple perspectives, integrating advances in human cognition, neuroimaging and computational modelling.Cambridge Trust Chinese Scholarship Counci

Using biologically plausible neural models to specify the functional and neural mechanisms of visual search

We review research from our laboratory that attempts to pull apart the functional and neural mechanisms of visual search using converging, inter-disciplinary evidence from experimental studies with normal participants, neuropsychological studies with brain lesioned patients, functional brain imaging and computational modelling. The work suggests that search is determined by excitatory mechanisms that support the selection of target stimuli, and inhibitory mechanisms that suppress irrelevant distractors. These mechanisms operate through separable though overlapping neural circuits which can be functionally decomposed by imposing model-based analyses on brain imaging data. The chapter highlights the need for inter-disciplinary research for understanding complex cognitive processes at several levels