An Optimal Solution for Test Case Generation Using ROBDD Graph and PSO Algorithm

Software testing is one of the most important techniques to examine the behavior of the software products to assure their quality. An effective and efficient testing approach must balance two important but conflicting requirements. One of them is the accuracy that needs a large number of test cases for testing, and the second one is reducing the time and cost, which requires a few test cases. Even for small software, the number of possible test cases is typically very large, and exhaustive testing is impractical. Hence, selecting appropriate test suite is necessary. Cause–effect graph testing is a common black‐box testing technique, which is equivalently representing Boolean relations between input parameters. However, the other traditional black‐box strategies cannot identify the relations that it may result in loss of some of the important test cases. Although the cause–effect graph is regarded very promising in specification testing, it is observed that most of the proposed approaches using the graph are complex or generate impossible and a large number of test cases. This observation has motivated our research to propose an efficient strategy to generate minimal test suite that simultaneously achieves high coverage of input parameters. To do so, at first, we identify major effects from the cause–effect graph using reduced ordered binary decision diagram (ROBDD). ROBDD makes the related Boolean expression of the graph concise and obtains a unique representation of the expression. Using the ROBDD, it is possible to reduce the size of the generated test suite and to perform testing faster. After that, our proposed method utilizes particle swarm optimization (PSO) algorithm to select the optimal test suite, which covers all pairwise combinations of input parameters. The experimental results show that our approach simultaneously achieves high efficacy and reduces cost of testing by selecting appropriate test cases, respectively, to both test size and coverage size. Also, it outperforms some existing state‐of‐the‐art strategies in the black‐box testing. Copyright © 2015 John Wiley & Sons, Ltd.     

An Efficient Solution for Model Checking Graph Transformation Systems

This paper presents an efficient solution for modeling checking graph transformation systems. The approach transforms AGG specifications into Bogor models and supports both attributed typed graphs and layered transformations. Resulting models are amenable to check interesting properties expressed as combinations of LTL (Linear Temporal Logic) and graph transformation rules. The first experimental results are encouraging and show that in most cases our proposal improves existing approaches, both in terms of performance and expressiveness.     

The effect of pH and calcium ion on rheological behaviour of β‐lactoglobulin‐basil seed gum mixed gels

Effect of pH (4.5–7.5) and Ca²⁺ (0.01–0.5 m ) on gelation of single and mixed systems of 10% β‐lactoglobulin (BLG) and 1% basil seed gum (BSG) was investigated. The gelling point of BLG and BSG gels was strongly pH‐dependent, and stiffer gels formed at higher pH. The BLG gels were formed upon heating to 90 °C and reinforced on cooling to 20 °C; however, the gelation of BSG occurred at temperatures below 70 °C. By increasing Ca²⁺ concentration, storage modulus of BLG and BSG gels were increased, although pH had a greater effect than Ca²⁺. In contrast, mixed systems showed two distinct types of behaviour: BLG gel formation and BSG network, suggesting that phase‐separated gels were formed. In addition, higher strength was obtained for BLG‐BSG mixture at higher Ca²⁺ concentration.     

Implementation intentions, occupational stress, and the exercise intention-behavior relationship.

Many individuals intend to exercise, but fail to link this intention to behavior. The present study examined the impact of an implementation intention intervention (i.e., instructions to form specific if-then plans) on an exercise intention-behavior relationship among working adults who varied in reported occupational stress levels. Results indicated that implementation intentions backfired, such that participants who did not form an implementation intention exercised significantly more than participants who formed an implementation intention. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Ultrasonography by emergency physicians in patients with suspected ureteral colic

We performed a prospective study of patients with suspected ureteral colic to evaluate the test characteristics of bedside renal ultrasonography (US) performed by emergency physicians (EPs) for detecting hydronephrosis, and to evaluate how US can be used to predict the likelihood of nephrolithiasis. Thirteen EPs performed US, recorded the presence of hydronephrosis, and made an assessment of the likelihood of nephrolithiasis. All patients underwent i.v. pyelography (IVP) or unenhanced helical computed tomography (CT). There were 126 patients in the study: 84 underwent IVP; 42 underwent helical CT. Test characteristics of bedside US for detecting hydronephrosis were: sensitivity 72%, specificity 73%, positive predictive value (PPV) 85%, negative predictive value (NPV) 54%, accuracy 72%. The PPV and NPV for the ability of the EP to predict nephrolithiasis after performing US were 86% and 75%, respectively. We conclude that bedside US performed by EPs may be used to detect hydronephrosis and help predict the presence of nephrolithiasis.     

Performance improvement for recommender systems using ontology

Recommender systems have emerged in the e-commerce domain and have been developed to actively recommend appropriate items to online users. The use of recently developed hybrid recommendation systems has helped overcome the main drawbacks of Content-Based Filtering (CBF) and Collaborative Filtering (CF). In hybrid recommendation systems that combine CF and CBF, the CF part uses two methods, including memory- and model-based approaches. Both approaches have some advantages and disadvantages for item recommendation. Sparsity has been one of the main difficulties associated with these approaches, whereas recommendation with high accuracy has been one of the important advantages of the memory-based approach. However, this approach is not scalable for current recommendation systems as their databases include huge numbers of items and users. In contrast, the model-based approach generates recommendations with low accuracy but is scalable for large databases in e-commerce recommender systems. Accordingly, to address this problem and take advantage of both approaches, in this work, we propose a new hybrid recommendation method and evaluate it using a real-world dataset. The aim is to improve efficiency and accuracy by designing a heuristic hybrid recommender method that combines memory-based and model-based approaches. Specifically, we use ontology in the CF part and improve ontology structure by eliminating uniformity of edges of the hierarchical relation between concepts (IS-A relation) in item ontology in the CBF part. Ontology structure is considered for improving accuracy; according to this, a new method for measuring semantic similarity that is more accurate than the traditional methods is presented. This new method can enhance the accuracy of CF and CBF in our method. In addition, the number of searches required to find similar clusters and neighbor users of the target user is decreased significantly using ontology, enhanced clustering and the new proposed algorithm. We evaluate the proposed method using a real-world dataset. The experimental results show that our method is more scalable and accurate than the benchmark k-Nearest Neighbor (k-NN) and model-based recommendation methods.     

The importance of Popper's theories to psychology.

In a comment on G. A. Kimble's (see record 1986-07921-001) and L. Krasner and A. C. Houts's (see record 1986-10225-001) articles on the place of values in psychological research, the present author argues that the theory of induction and the entire program of the Vienna Circle of logical positivists were destroyed by the theory of objective knowledge of K. R. Popper (published between 1934 and 1982). It is suggested that when Popper's ideas are understood, there can be an epistemic armistice between the scientific and humanistic cultures, although both sides will need to modify some assumptions that they currently share. (22 ref) (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

A P2P Botnet detection scheme based on decision tree and adaptive multilayer neural networks

In recent years, Botnets have been adopted as a popular method to carry and spread many malicious codes on the Internet. These malicious codes pave the way to execute many fraudulent activities including spam mail, distributed denial-of-service attacks and click fraud. While many Botnets are set up using centralized communication architecture, the peer-to-peer (P2P) Botnets can adopt a decentralized architecture using an overlay network for exchanging command and control data making their detection even more difficult. This work presents a method of P2P Bot detection based on an adaptive multilayer feed-forward neural network in cooperation with decision trees. A classification and regression tree is applied as a feature selection technique to select relevant features. With these features, a multilayer feed-forward neural network training model is created using a resilient back-propagation learning algorithm. A comparison of feature set selection based on the decision tree, principal component analysis and the ReliefF algorithm indicated that the neural network model with features selection based on decision tree has a better identification accuracy along with lower rates of false positives. The usefulness of the proposed approach is demonstrated by conducting experiments on real network traffic datasets. In these experiments, an average detection rate of 99.08 % with false positive rate of 0.75 % was observed.

     

Scenario-driven analysis of systems specified through graph transformations

Model checking is one of the most accurate analysis techniques which are used to verify software and hardware systems. However, the analysis of large and complex systems tends to become infeasible since their state spaces easily become too big. Besides well-known abstraction techniques, which may hamper the accuracy of results, in this paper we propose the use of scenario-driven model checking to address and mitigate the state explosion problem. The proposal starts from systems specified through a Graph Transformation (GT) system and it is focused on the analysis of the most significant scenarios. We exploit the modularity of GT systems to reduce the state space by eliminating all the nodes and rules that are not involved in the scenario. Focused analysis also helps concentrate on the most critical behaviors of the system and smooth the risks associated with them. The paper introduces the analysis approach and explains how scenarios (specified in terms of sequence diagrams) can help to reduce the state space. All main concepts are illustrated through a simple application for a travel agency specified as if it were a service-oriented application.