A Prioritization Approach for Regression Test Cases Based on a Revised Genetic Algorithm

The regression testing is a software-based testing approach executed to verify that changes made to the softwaredo not affect the existing functionality of the product. On account of the constraints of time and cost, it isimpractical to re-execute all the test cases for software whenever a change occurs. In order to overcome sucha problem in the selection of regression test cases, a prioritization technique should be employed. On the basisof some predefined criterion, the prioritization techniques create an execution schedule for the test cases, sothe higher priority test cases can be performed earlier than the lower priority test cases in order to improvethe efficiency of the software testing. Many prioritization criteria for regression test cases have been proposedin software testing literature; however, most of such techniques are code-based. Keeping in view this fact, thisresearch work has proposed a prioritization approach for regression test cases generated from software specificationswhich are based on the criterion of the Average Percentage Transition Coverage (APTC) by using arevised genetic algorithm. This criterion evaluates the rate of transitions coverage by incorporating knowledgeabout the significance of transitions between activates in the form of weights. APTC has been used as a fitnessevaluation function in a genetic algorithm to measure the effectiveness of a test cases sequence. Moreover, inorder to improve the coverage percentage, the proposed approach has revised the genetic algorithm by solvingthe problem of the optimal local solution. The experimental results show that the proposed approach demonstratesa good coverage performance with less execution time as compared to the standard genetic algorithmand some other prioritization techniques.

Self-Evolving Fuzzy Controller Composed of Univariate Fuzzy Control Rules

The paper proposes a methodology to online self-evolve direct fuzzy logic controllers (FLCs), to deal with unknown and time-varying dynamics. The proposed methodology self-designs the controller, where fuzzy control rules can be added or removed considering a predefined criterion. The proposed methodology aims to reach a control structure easily interpretable by human operators. The FLC is defined by univariate fuzzy control rules, where each input variable is represented by a set of fuzzy control rules, improving the interpretability ability of the learned controller. The proposed self-evolving methodology, when the process is under control (online stage), adds fuzzy control rules on the current FLC using a criterion based on the incremental estimated control error obtained using the system’s inverse function and deletes fuzzy control rules using a criterion that defines “less active” and “less informative” control rules. From the results on a nonlinear continuously stirred tank reactor (CSTR) plant, the proposed methodology shows the capability to online self-design the FLC by adding and removing fuzzy control rules in order to successfully control the CSTR plant.

Preregistering Qualitative Research: A Delphi Study

Preregistrations -- records made a priori about study designs and analysis plans and placed in open repositories -- are thought to strengthen the credibility and transparency of research. Different authors have put forth arguments in favor of introducing this practice in qualitative research and made suggestions for what to include in a qualitative preregistration form. The goal of this study was to gauge and understand what parts of preregistration templates qualitative researchers would find helpful and informative. We used an online Delphi study design consisting of two rounds with feedback reports in between. In total, 48 researchers participated (response rate: 16%). In round 1, panelists considered 14 proposed items relevant to include in the preregistration form, but 2 items had relevance scores below our predefined criterion (68%) with mixed argument and were put forth again. We combined items where possible, leading to 11 revised items. In round 2, panelists agreed on including the two remaining items. Panelists also converged on suggested terminology and elaborations, except for two terms for which they provided clear arguments. The result is an agreement-based form for the preregistration of qualitative studies that consists of 13 items. The form will be made available as a registration option on Open Science Framework (osf.io). We believe it is important to assure that the strength of qualitative research, which is its flexibility to adapt, adjust and respond, is not lost in preregistration. The preregistration should provide a systematic starting point.

Preregistering Qualitative Research: A Delphi Study

Preregistrations—records made a priori about study designs and analysis plans and placed in open repositories—are thought to strengthen the credibility and transparency of research. Different authors have put forth arguments in favor of introducing this practice in qualitative research and made suggestions for what to include in a qualitative preregistration form. The goal of this study was to gauge and understand what parts of preregistration templates qualitative researchers would find helpful and informative. We used an online Delphi study design consisting of two rounds with feedback reports in between. In total, 48 researchers participated (response rate: 16%). In round 1, panelists considered 14 proposed items relevant to include in the preregistration form, but two items had relevance scores just below our predefined criterion (68%) with mixed argument and were put forth again. We combined items where possible, leading to 11 revised items. In round 2, panelists agreed on including the two remaining items. Panelists also converged on suggested terminology and elaborations, except for two terms for which they provided clear arguments. The result is an agreement-based form for the preregistration of qualitative studies that consists of 13 items. The form will be made available as a registration option on Open Science Framework (osf.io). We believe it is important to assure that the strength of qualitative research, which is its flexibility to adapt, adjust and respond, is not lost in preregistration. The preregistration should provide a systematic starting point.

Improvement inEnergy Efficiency of Wireless Sensor Network

Clustering in wireless sensor network is important to increase the lifetime of sensor network. LEACH protocol is one of the clustering routing protocols in wireless sensor networks. In LEACH each node has the equal probability to be a cluster head, due to which the energy dissipation of every node is balanced. In LEACH protocol, time is divided into many rounds and in each round, all the nodes wishes to be cluster head according to a predefined criterion. This paper focuses on the approach that how could the number of cluster heads are limited in the network, if we limit the number of cluster head to a percentage of total nodes in the network, we can increase the lifetime of the network and decrease the energy dissipation per node. These functions can be used to enhance the performance of cluster-based wireless sensor networks in terms of lifetime and throughput.

CLUSTERING WITH MINIMUM SPANNING TREES

We propose two Euclidean minimum spanning tree based clustering algorithms — one a k-constrained, and the other an unconstrained algorithm. Our k-constrained clustering algorithm produces a k-partition of a set of points for any given k. The algorithm constructs a minimum spanning tree of a set of representative points and removes edges that satisfy a predefined criterion. The process is repeated until k clusters are produced. Our unconstrained clustering algorithm partitions a point set into a group of clusters by maximally reducing the overall standard deviation of the edges in the Euclidean minimum spanning tree constructed from a given point set, without prescribing the number of clusters. We present our experimental results comparing our proposed algorithms with k-means, X-means, CURE, Chameleon, and the Expectation-Maximization (EM) algorithm on both artificial data and benchmark data from the UCI repository. We also apply our algorithms to image color clustering and compare them with the standard minimum spanning tree clustering algorithm as well as CURE, Chameleon, and X-means.

Results of a randomized phase III study to compare the overall survival of gefitinib (IRESSA) versus docetaxel in Japanese patients with non-small cell lung cancer who failed one or two chemotherapy regimens

LBA7509 Background: This phase III study (V-15–32) compared gefitinib vs docetaxel on overall survival (OS) in Japanese patients (pts) with pretreated advanced NSCLC. Methods: Pts with advanced or metastatic (Stage IIIb or IV) NSCLC who failed 1 or 2 chemotherapy regimens were randomized to gefitinib (250 mg/day) or docetaxel (60 mg/m2 every 3 weeks). Non-inferiority of the primary endpoint, OS, was assessed by the confidence interval (CI) of the hazard ratio (HR; gefitinib/docetaxel) derived from an unadjusted Cox proportional hazard model. Results: 489 eligible pts were recruited. Non-inferiority in OS was not achieved (HR 1.12; 95.24% CI 0.89, 1.40) according to predefined criterion (upper CI limit for HR <1.25); however, no significant difference in OS (p=0.330) or PFS (p=0.335) was apparent between treatments. Post study, 36% of gefitinib-treated pts received subsequent docetaxel and 40% received no other therapy apart from gefitinib; 53% of docetaxel-treated pts received subsequent gefitinib and 26% received no other therapy apart from docetaxel. Gefitinib significantly improved ORR (22.5% vs 12.8%; p=0.009), TTF (HR 0.63; 95% CI 0.51, 0.77; p<0.001), and QoL (FACT-L trial outcome index 20.5% vs 8.7%; p=0.002; FACT-L 23.4% vs 13.9%; p=0.023), vs docetaxel. Additional subgroup analyses will be presented. Grade 3/4 AEs occurred in 40.6% (gefitinib) and 81.6% (docetaxel) of pts. Incidence of interstitial lung disease (ILD) was 5.7% (n=14) and 2.9% (n=7), respectively. There were 4 deaths due to AEs in the gefitinib arm (3 possibly treatment-related due to ILD; 1 due to pneumonia that was not considered treatment-related) and none in the docetaxel arm. Conclusions: Whilst non-inferiority in OS between gefitinib and docetaxel was not demonstrated according to predefined criteria, there was no statistically significant difference in survival between the two arms. Secondary endpoints largely unaffected by subsequent therapy provide further evidence of clinical efficacy of gefitinib in these pts. AEs were consistent with those previously observed for both treatments. [Table: see text]

VISUAL APPROACH TO SUPERVISED VARIABLE SELECTION BY SELF-ORGANIZING MAP

Practical data analysis often encounters data sets with both relevant and useless variables. Supervised variable selection is the task of selecting the relevant variables based on some predefined criterion. We propose a robust method for this task. The user manually selects a set of target variables and trains a Self-Organizing Map with these data. This sets a criterion to variable selection and is an illustrative description of the user's problem, even for multivariate target data. The user also defines another set of variables that are potentially related to the problem. Our method returns a subset of these variables, which best corresponds to the description provided by the Self-Organizing Map and, thus, agrees with the user's understanding about the problem. The method is conceptually simple and, based on experiments, allows an accessible approach to supervised variable selection.