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Requirements form the basis for defining software systems' obligations and tasks. Testable requirements help prevent failures, reduce maintenance costs, and make it easier to perform acceptance tests. However, despite the importance of measuring and quantifying requirements testability, no automatic approach for measuring requirements testability has been proposed based on the requirements smells, which are at odds with the requirements testability. This paper presents a mathematical model to evaluate and rank the natural language requirements testability based on an extensive set of nine requirements smells, detected automatically, and acceptance test efforts determined by requirement length and its application domain. Most of the smells stem from uncountable adjectives, context-sensitive, and ambiguous words. A comprehensive dictionary is required to detect such words. We offer a neural word embedding technique to generate such a dictionary automatically. Using the dictionary, we could automatically detect Polysemy smell (domain-specific ambiguity) for the first time in 10 application domains. Our empirical study on nearly 1000 software requirements from six well-known industrial and academic projects demonstrates that the proposed smell detection approach outperforms Smella, a state-of-the-art tool, in detecting requirements smells. The Precision and Recall of smell detection are improved with an average of 0.03 and 0.33, respectively, compared to the state-of-the-art. The proposed requirement testability model measures the testability of 985 requirements with a mean absolute error of 0.12 and a mean squared error of 0.03, demonstrating the model's potential for practical use. [ABSTRACT FROM AUTHOR]

Path testing is one of the most efficient approaches for covering a program during the test. However, executing a path with a single or limited number of test data does not guarantee that the path is fault-free, specifically in the fault-prone paths. A common solution in these cases is to extract the corresponding domain of the path constraint that covers the path and select the arbitrary numbers of test data from the extracted domain. Many approaches have been proposed to extract and partition the program input domain statically without executing the program under test. However, extracting domain based on the path constraints is inaccurate and time-consuming, specifically in high dimensional inputs. This paper presents a dynamic approach for extracting the input domain of a given path based on the modified Markov chain Monte Carlo method, multi-agent Metropolis algorithm. The algorithm uses sampling and randomly walks around symmetric proposal distribution to find a finite number of points that approximate the target domain. Our approach chooses the highest fault-prone path from the minimum-cost test paths for domain extraction to maximize the fault detection capabilities. Our experiments with ten Python programs reveal a 14% improvement in the accuracy of the extracted domains compared to the state-of-the-art domain extraction approaches. In addition, the mutation score of automatically generated test cases has improved by an average of 17.95% compared to branch and prime-path testing approaches. [ABSTRACT FROM AUTHOR]

The role of inflammation has been proven in acute myocardial infarction (AMI) pathogenesis. Due to the effect of NLRP3 gene expression in the inflammation process of MI, we aimed to explore the expression changes and diagnostic power of four inflammation-related miRNAs including miR-17-3p, miR-101-3p, miR-335-3p, miR-296-3p and their potential target, NLRP3, in ST-segment elevation myocardial infarction (STEMI), and non-STEMI (NSTEMI) patients as two major classes of AMI. The expression level of these genes were evaluated in 300 participants equally divided into three groups of STEMI, NSTEMI, and control using quantitative real-time PCR. The expression level of NLRP3 was upregulated in STEMI and NSTEMI patients compared to control subjects. Besides, the expression levels of miR-17-3p, miR-101-3p, and miR-296-3p were significantly downregulated in STEMI and NSTEMI patients compared to controls. The increased expression of NLRP3 had a very strong inverse correlation with miR-17-3p in patients with STEMI and with miR-101-3p in the STEMI and NSTEMI patients. ROC curve analysis showed that the expression level of miR-17-3p had the highest diagnostic power for discrimination between STEMI patients and controls. Remarkably, the combination of all markers resulted in a higher AUC. In summary, there is a significant association between the expression levels of miR-17-3p, miR-101-3p, miR-335-3p, miR-296-3p, and NLRP3 and the incidence of AMI. Although the miR-17-3p expression level has the highest diagnostic power to distinguish between STEMI patients and control subjects, the combination of these miRNAs and NLRP3 could serve as a novel potential diagnostic biomarker of STEMI. [ABSTRACT FROM AUTHOR]

Novel metaheuristic algorithms are now considered an appealing collection of methods for solving complex optimization problems, in which the challenging objective is to find a better solution in a shorter computation time. Focusing on the same objective, this paper proposes a novel metaheuristic optimization algorithm inspired by heat transfer relationships based on the second law of thermodynamics. Imitating the heat transfer behavior of solid objects, the proposed method is called the heat transfer relation-based optimization algorithm (HTOA). This behavior was modeled on a heat transfer function used to measure temperature differences between the selected solutions and the best solution. This function was employed to determine and add the heat capacity transferred between those solutions. Finally, all the solutions were heat-exchanged with the best solution to select the fittest solution and exclude the rest. This procedure continued until the best solution or solutions were found. The proposed method is challenged by many famous benchmark problems in two categories as well as two real-world problems (PID controller and linear regression). The HTOA was then compared with a number of well-known and state-of-the-art optimization algorithms. Selecting better solutions and requiring shorter computation time, the proposed HTOA outperformed the other algorithms. [ABSTRACT FROM AUTHOR]

Background/objectives: Weight loss through a low-calorie diet (LCD) could improve low-grade inflammation evident in the obese state. Few studies have evaluated the effect of the mixed nuts consumption in the context of a LCD on inflammatory biomarkers. This study compared the effects of a nut-enriched LCD (NELCD) with a nut-free LCD (NFLCD) on body weight and inflammatory markers in overweight or obese coronary artery disease (CAD) patients.Subjects/method: In this randomized controlled parallel trial, patients with stable CAD of both genders were randomly allocated to 8-week NELCD or NFLCD. Body weight, plasma C-reactive protein (CRP), interleukin-6 (IL-6), interleukin 10 (IL-10), intercellular adhesion molecule-1 (ICAM-1), and monocyte chemoattractant protein (MCP-1) were assessed at baseline and 8 weeks.Results: Overall, 67 patients (aged 58.8 ± 7.4 years; BMI 30.9 ± 3.9 kg/m2) completed the study. Participants in both groups lost weight to a comparable extent. Patients in the NELCD group showed a decrease in ICAM-1 (p = 0.04) and IL-6 (p = 0.02) concentrations compared to NFLCD group. No significant difference in concentrations of MCP-1, IL-10, or CRP was observed between diet groups.Conclusions: Nuts are healthy energy-dense foods that if included in controlled amounts in a weight management program can still result in weight reduction and may improve some plasma concentration of inflammatory factors, such as ICAM-1 and IL-6. [ABSTRACT FROM AUTHOR]

Appropriate test data are a crucial factor to succeed in fuzz testing. Most of the real-world applications, however, accept complex structure inputs containing data surrounded by meta-data which is processed in several stages comprising of the parsing and rendering (execution). The complex structure of some input files makes it difficult to generate efficient test data automatically. The success of deep learning to cope with complex tasks, specifically generative tasks, has motivated us to exploit it in the context of test data generation for complicated structures such as PDF files. In this respect, a neural language model (NLM) based on deep recurrent neural networks (RNNs) is used to learn the structure of complex inputs. To target both the parsing and rendering steps of the software under test (SUT), our approach generates new test data while distinguishing between data and meta-data that significantly improve the input fuzzing. To assess the proposed approach, we have developed a modular file format fuzzer, IUST-DeepFuzz. Our experimental results demonstrate the relatively high coverage of MuPDF code by our proposed fuzzer, IUST-DeepFuzz, in comparison with the state-of-the-art tools such as learn&fuzz, AFL, Augmented-AFL, and random fuzzing. In summary, our experiments with many deep learning models revealed the fact that the simpler the deep learning models applied to generate test data, the higher the code coverage of the software under test will be. [ABSTRACT FROM AUTHOR]

Fault localization is one of the most important and difficult tasks in the software debugging process. Therefore, several methods have been proposed to automate and improve this process. Mutation-based fault localization is one of the states of the art techniques that try to locate faults by executing different mutants of the faulty program. In addition to favorable results, it is along with a massive increase in mutation execution cost. In this paper, we propose a new mutation-based fault localization approach called SMBFL, that aim to reduce the execution cost by reducing the number of statements to be mutated. As fewer mutants execute with SMBFL, the whole process will become faster and the cost will decrease. SMBFL only examines the statements in the dynamic slice of the program under test. The statements that present in the dynamic slice have a direct effect on the execution of the program with the specified test case. In the SMBFL method, the suspiciousness score of program statements is measured based on the entropy of their mutants. The proposed formula, MuEn, determines the suspiciousness score based on the result of executing mutants of each statement of the program. SMBFL is evaluated during a series of tests. The results show a relative increase in the accuracy of fault localization, by an average of 14.2%, and a decrease in the execution time of the fault localization process, by an average of 24.3%. Finally, the MuEn formula applies the least execution overhead to the fault localization process. [ABSTRACT FROM AUTHOR]

Test data adequacy is a major challenge in software testing literature. The difficulty is to provide sufficient test data to assure the correctness of the program under test. Especially, in the case of latent faults, the fault does not reveal itself unless specific combinations of input values are used to run the program. In this respect, detection of subdomains of the input domain that cover a specific execution path seems promising. A subdomain covers an execution path provided that each test data taken from the subdomain satisfies the path constraint. Dynamic Domain Reduction, or DDR in short, is a very well-known test data generation procedure, targeted at detection of the subdomains of the input domain that satisfy a given path constraint. In this paper, an improved version of DDR called Rapid Dynamic Domain Reduction, or RDDR in short, is introduced. RDDR is intended to explore subdomains of a program input domain, satisfying a given path constraint. For each feasible path, there is a distinct subdomain of the input domain that causes the program to execute the path. Hereby, we introduce a new metric named domain coverage, to qualify input data sets, in terms of the percentage of the subdomain of a feasible path covered by the data set. The main inspiration behind the domain coverage metric is to support test data adequacy. Our empirical results based on some well-known case studies confirms that RDDR significantly outperforms DDR in terms of speed and accuracy. [ABSTRACT FROM AUTHOR]

Rapid online adaptation to the new business requirements can improve innovation level and market competency of collaborative organizations. Complex and unstructured processes are provision e-services in collaborative networks through web service inter-connections, which unanticipated changes made it hard to manage them. In a cross-organizational domain, when partners deal with unexpected changes, the received requests are represented as complex theories, and business process adaptation will be more complicated in the occurrence of concept drifts. Rapid prediction and adaptation to new situations need concept drift detection and novel class prediction mechanism for the overall collaborative network processes in both data and control flows. In this article, a new approach to the online reflection of distributed rule concept drifts of collaborative network reference processes is introduced. The solution to the data-informed adaptation of unstructured process employed managing rule concept drifts and cross-organizational processes restructuring via the distributed model with minor updates. The method could react to the new events via an ensemble prediction mechanism. Its architecture has major components for monitoring, dynamic distributed rule reconfiguration, and partners side components. The paper suggests a service-oriented semantic network of distributed rules for e-services provision, substitution, and replacement via management of choreographed web-services. The approach has been validated and verified with real data belonging to the healthcare domain. The results prove the adaptation mechanism efficiency of the daily changes. [ABSTRACT FROM AUTHOR]

In this paper, a novel approach, Inforence, is proposed to isolate the suspicious codes that likely contain faults. Inforence employs a feature selection method, based on mutual information, to identify those bug-related statements that may cause the program to fail. Because the majority of a program faults may be revealed as undesired joint effect of the program statements on each other and on program termination state, unlike the state-of-the-art methods, Inforence tries to identify and select groups of interdependent statements which altogether may affect the program failure. The interdependence amongst the statements is measured according to their mutual effect on each other and on the program termination state. To provide the context of failure, the selected bug-related statements are chained to each other, considering the program static structure. Eventually, the resultant cause-effect chains are ranked according to their combined causal effect on program failure. To validate Inforence, the results of our experiments with seven sets of programs include Siemens suite, gzip, grep, sed, space, make and bash are presented. The experimental results are then compared with those provided by different fault localization techniques for the both single-fault and multi-fault programs. The experimental results prove the outperformance of the proposed method compared to the state-of-the-art techniques. [ABSTRACT FROM AUTHOR]

In this article, a new criterion, domain coverage, for white box testing is introduced. In search of the biggest subdomains for input variables exercising a given path, the variable domains are broken down into overlapping subregions in such a way that the resultant subregions either exercise or deviate from the path. To this aim, an incremental method is applied to detect the subregions, covering the longest subpaths from the start of the given path. The detected subdomain is further subdivided, and this process is repeated as far as the path is fully covered. If no subdomain is detected, the method is backtracked. The backtrack begins with the subdomain covering the longest subpath at the beginning of the given path, but this time the subdomain is divided into relatively smaller subregions, compared with the last time. A directed acyclic graph representation of each input variable domain is used to ignore re-examination of those subdomains which deviate from the path. Conducted experiments reveal that our proposed domain-oriented algorithm outperforms the existing path-oriented test data generation algorithms. [ABSTRACT FROM AUTHOR]

Traditional test case generation approaches focus on design and implementation models while a large percentage of software errors are caused by the lack of understanding in the early phases. One of the most important models in the early phases of software development is business process model which closely resembles the real world and captures the requirements precisely. The aim of this paper is to present a model-based approach to automatically generate test cases from business process models. We first model business processes and convert them to state graphs. Then, the graphs are traversed and transformed to the input format of the "Spec explorer" tool that generates the test cases. Furthermore, we conduct a study to evaluate the impact of process characterizations on the performance of the proposed method. [ABSTRACT FROM AUTHOR]

Despite the proven applicability of the spectrum-based fault localization (SBFL) methods, their effectiveness may be degraded due to the presence of coincidental correctness, which occurs when faults fail to propagate, i.e., their execution does not result in failures. This article aims at improving SBFL effectiveness by mitigating the effect of coincidentally correct test cases. In this regard, given a test suite in which each test has been classified as failing or passing and each faulty program has a single-bug, we present a program slicing-based technique to identify a set of program entities that directly affect the program output when executed with failing test cases, called failure candidate causes (FCC). We then use FCC set to identify test cases that can be marked as being coincidentally correct. These tests are identified based on two heuristics: the average suspiciousness score of the statements that directly affect the program output and the coverage ratio of those statements. To evaluate our approach, we used several evaluation metrics and conducted extensive experiments on programs containing single and multiple bugs, including both real and seeded faults. The empirical results demonstrate that the proposed heuristics can alleviate the coincidental correctness problem and improve the accuracy of SBFL techniques. [ABSTRACT FROM AUTHOR]

Introduction: To prevent stroke, anticoagulants should be administered after calculation of CHA2DS2-VASc and HAS-BLED scores in patients with Atrial Fibrillation (AF); nonetheless, these scores are sometimes neglected in clinical settings.Aim: The present study was designed to assess agreement of anticoagulant therapy according to clinicians and CHA2DS2-VASc and HAS-BLED scores in Iranian AF patients in Moddares Hospital.Methods: AF patients were diagnosed according to clinical history, clinical examination, and electrocardiogram. Data including the anticoagulant prescription according to clinicians were recorded. CHA2DS2-VASc and HAS-BLED scores were then calculated for each patient. Agreement of anticoagulant therapy according to clinicians and CHA2DS2-VASc and HAS-BLED scores was analyzed using Cohen’s kappa coefficient.Results: 97.5% of the patients (n = 117) were appropriately (according CHA2DS2-VASc and HAS-BLED scores) treated with anticoagulants by clinicians, notwithstanding a 2.5% of patients with inappropriate anticoagulant therapy (n = 3). The Cohen’s kappa coefficient was 0.81 (P = 0.0).Conclusions: The findings of the present study suggest an almost perfect agreement between anticoagulant therapy according to clinicians and that according to CHA2DS2-VASc and HAS-BLED scores in the studied population. [ABSTRACT FROM AUTHOR]

Software testing is a fundamental activity in the software development process aimed to determine the quality of software. To reduce the effort and cost of this process, defect prediction methods can be used to determine fault-prone software modules through software metrics to focus testing activities on them. Because of model interpretation and easily used by programmers and testers some recent studies presented classification rules to make prediction models. This study presents a rule-based prediction approach based on kernel k-means clustering algorithm and Distance based Multi-objective Particle Swarm Optimization (DSMOPSO). Because of discrete search space, we modified this algorithm and named it DSMOPSO-D. We prevent best global rules to dominate local rules by dividing the search space with kernel k-means algorithm and by taking different approaches for imbalanced and balanced clusters, we solved imbalanced data set problem. The presented model performance was evaluated by four publicly available data sets from the PROMISE repository and compared with other machine learning and rule learning algorithms. The obtained results demonstrate that our model presents very good performance, especially in large data sets. [ABSTRACT FROM AUTHOR]

This paper proposes a flow analysis approach to provide accurate flow facts such as number of loop iterations and infeasible paths for WCET analysis. To achieve this, a novel approach to model program flows of given loops as compositional condition of controls that can model the program flow of the sequence of basic blocks on an execution path through a single loop iteration is proposed. The proposed approach builds distinct symbolic expressions each representing a path condition as conjunctions of branch conditions along the path. Then, the path conditions are formed via substituting all constituent variables of the branch conditions with symbolic expressions computing the value of these variables. The final symbolic expression will contain variables denoting theirs value at the loop entry point. Considering the change in the value of the loop variables of the path condition, the feasible values of these variables are computed and consequently the number of the loop iterations along the iteration path is determined. Applying a SMT solver to the symbolic expressions representing each path condition, all the infeasible paths along the loop body are detected. The results of applying our flow analysis approach to a number of programs addressed in the Mälardalen benchmark suite reveal the capability and efficiency of our proposed approach. [ABSTRACT FROM AUTHOR]

Considering the fact that faults may be revealed as undesired mutual effect of program predicates on each other, a new approach for localizing latent bugs, namely Hierarchy-Debug, is presented in this paper. To analyze the vertical effect of predicates on each other and on program termination status, the predicates are fitted into a logistic lasso model. To support scalability, a hierarchical clustering algorithm is applied to cluster the predicates according to their presence in different executions. Considering each cluster as a pseudo-predicate, a distinct lasso model is built for intermediate levels of the hierarchy. Then, we apply a majority voting technique to score the predicates according to their lasso coefficients at different levels of the hierarchy. The predicates with relatively higher scores are ranked as fault relevant predicates. To provide the context of failure, faulty sub-paths are identified as sequences of fault relevant predicates. The grouping effect of Hierarchy-Debug helps programmers to detect multiple bugs. Four case studies have been designed to evaluate the proposed approach on three well-known test suites, Space, Siemens, and Bash. The evaluations show that Hierarchy-Debug produces more precise results compared with prior fault localization techniques on the subject programs. [ABSTRACT FROM AUTHOR]

Prescheduling algorithms are targeted at restructuring of task graphs for optimal scheduling. Task graph scheduling is a NP-complete problem. This article offers a prescheduling algorithm for tasks to be executed on the networks of homogeneous processors. The proposed algorithm merges tasks to minimize their earliest start time while reducing the overall completion time. To this end, considering each task as a player attempting to reduce its earliest time as much as possible, we have applied the idea of Nash equilibrium in game theory to determine the most appropriate merging. Also, considering each level of a task graph as a player, seeking for distinct parallel processors to execute each of its independent tasks in parallel with the others, the idea of Nash equilibrium in game theory can be applied to determine the appropriate number of processors in a way that the overall idle time of the processors is minimized and the throughput is maximized. The communication delay will be explicitly considered in the comparisons. Our experiments with a number of known benchmarks task graphs and also two well-known problems of linear algebra, LU decomposition and Gauss-Jordan elimination, demonstrate the distinguished scheduling results provided by applying our algorithm. In our study, we consider ten scheduling algorithms: min-min, chaining, A, genetic algorithms, simulated annealing, tabu search, HLFET, ISH, DSH with task duplication, and our proposed algorithm (PSGT). [ABSTRACT FROM AUTHOR]

In achieving high performance on modern architectures it is critical to make effective use of the memory hierarchy. There are compiler-directed locality enhancement techniques that allow the transformation of program to achieve a higher locality: loop transformations, which are constrained by data dependences and data layout transformations, which have a global impact on the program locality. Due to these drawbacks, there must be a unification of the two techniques to achieve the benefits of both. In this paper, a novel unification of these techniques is presented. Using a model based on parameterized polyhedra and introducing new concepts, we propose a data locality optimization algorithm. In comparison with the other approaches, the technique proposed is capable of solving more conflicts and optimizing more references, a subtle way is proposed to optimize incompatible references to the same array, in the same loop, and also references in a cycle in the interference graph. Using parameterized cost functions, our technique estimates the importance of each sub-graph and optimizes data locality. Our experimental results show a significant improvement over the prior approaches. [ABSTRACT FROM AUTHOR]

In this paper, a mathematical solution for reducing the mean number of the tasks waiting to be processed in a grid environment is proposed. The approach uses queuing theory and models the grid environment in the form of an open queuing network (QN). Applying the steady state analysis to the proposed QN and minimizing the mean number of waiting tasks within the grid environment, an equality and inequality system is obtained. Solving the equality and inequality system, subtasks arrival rates at each of the resources within the grid environment can be estimated. Applying the obtained subtasks arrival rates at each of the grid resources, the mean number of the waiting tasks in the grid environment could be minimized. To evaluate the results obtained from the proposed QN and provide a graphical representation of the grid environment a formal description of the environment in terms of generalized stochastic Petri nets (GSPNs) is presented. Steady state analyzing of the GSPN model and finding the subtask dispatching weights at each of the grid resources, subtask arrival rates can be estimated. Comparing the results obtained from two proposed approaches shows that the subtasks arrival rates achieved from QNs and GSPNs are the same. [ABSTRACT FROM AUTHOR]

A Grid-Federation environment is composed of a collection of autonomous and selfish distributed cluster resource managers. These selfish managers participate in Grid-Federation to share their resources. Market models could be used to motivate the self-interested participants to share their resources. In this paper, firstly, a market for resource exchange in grid federation environment is established. Then, in order that the market reaches a Walrasian equilibrium, a computationally tractable mechanism is proposed. A Walrasian equilibrium problem consists of finding a set of prices and allocations of resources in such a way that the cluster resource managers could maximize their utilities and the market clears. Market clears when the resource supply equals to the demand. We show that in a Walrasian equilibrium, the Grid Federation market reaches an efficient resource allocation. [ABSTRACT FROM AUTHOR]

Loop tiling is an efficient loop transformation, mainly applied to detect coarse-grained parallelism in loops. It is a difficult task to apply n-dimensional non-rectangular tiles to generate parallel loops. This paper offers an efficient scheme to apply non-rectangular n-dimensional tiles in non-rectangular iteration spaces, to generate parallel loops. In order to exploit wavefront parallelism efficiently, all the tiles with equal sum of coordinates are assumed to reside on the same wavefront. Also, in order to assign parallelepiped tiles on each wavefront to different processors, an improved block scheduling strategy is offered in this paper. [ABSTRACT FROM AUTHOR]

Tiling is a known problem especially in the field of computational geometry and its related engineering branches. In fact, a tile is a set of points in the Cartesian space. The goal is to partition the space of the points as tiles with optimal dimensions and shapes such that a number of predefined semantic relations holds amongst the tiles. So far, this problem has been solved in special cases with two or three dimensions. The problem of determining the optimal tile is an NP-Hard problem. Presenting a novel constraint genetic algorithm in this paper, we have been able to solve the tiling problem in Cartesian spaces with more than two dimensions, for the loop parallelization problem. [ABSTRACT FROM AUTHOR]

Loop parallelization is an important issue in the acceleration of the execution of scientific programs. To exploit parallelism in loops a system of equations representing the dependencies between the loop iterations and a system of non-equations indicating the loop boundary conditions has to be solved. This is a NP-Complete problem. Our major contribution in this paper has been to apply genetic algorithm to solve system of equation and non-equation resulted from loop dependency analysis techniques to find two dependent loop iterations. We use distance vector to find the rest of dependencies. [ABSTRACT FROM AUTHOR]