A Simplified-Syntax-Based Static Structure Model for Embedded Software Analysis

In order to solve the problem that the embedded software has the shortcoming of the platform dependence, this paper presents an embedded software analysis method based on the static structure model. Before control flow and data flow analysis, a lexical analysis/syntax analysis method with simplified grammar and sentence depth is designed to analyze the embedded software. The experiments use the open source code of smart meters as a case, and the artificial faults as the test objects, repeating 30 times. Compared with the popular static analyzing tools PC-Lint and Splint, the method can accurately orient 91% faults, which is between PC-Lint＇s 95% and Splint＇s 85%. The result indicates that the correct rate of our method is acceptable. Meanwhile, by removing the platform-dependent operation with simplified syntax analysis, our method is independent of development environment. It also shows that the method is applicable to the compiled C（including embedded software） program.

Analysis software for upper atmospheric data developed by the IUGONET project and its application to polar science

To comprehensively understand the Arctic and Antarctic upper atmosphere, it is often crucial to analyze various data that are obtained from many regions. Infrastructure that promotes such interdisciplinary studies on the upper atmosphere has been developed by a Japanese inter-university project called the Inter-university Upper atmosphere Global Observation Network （1UGONET）. The objective of this paper is to describe the infrastructure and tools developed by IUGONET. We focus on the data analysis software. It is written in Interactive Data Language （IDL） and is a plug-in for the THEMIS Data Analysis Software suite （TDAS）, which is a set of IDL libraries used to visualize and analyze satellite- and ground-based data. We present plots of upper atmospheric data provided by IUGONET as examples of applications, and verify the usefulness of the software in the study of polar science. We discuss IUGONET＇s new and unique developments, i.e., an executable file of TDAS that can run on the IDL Virtual Machine, IDL routines to retrieve metadata from the IUGONET database, and an archive of 3-D simulation data that uses the Common Data Format so that it can easily be used with TDAS.

A Software Risk Analysis Model Using Bayesian Belief Network

The uncertainty during the period of software project development often brings huge risks to contractors and clients. If we can find an effective method to predict the cost and quality of software projects based on facts like the project character and two-side cooperating capability at the beginning of the project,we can reduce the risk. Bayesian Belief Network(BBN) is a good tool for analyzing uncertain consequences, but it is difficult to produce precise network structure and conditional probability table.In this paper,we built up network structure by Delphi method for conditional probability table learning,and learn update probability table and nodes’confidence levels continuously according to the application cases, which made the evaluation network have learning abilities, and evaluate the software development risk of organization more accurately.This paper also introduces EM algorithm, which will enhance the ability to produce hidden nodes caused by variant software projects.

Development of SPM Quantitative Micromorphology Analysis Software

Scanning Probe Microscope (SPM) has great advantages in quantitative micromorphology analysis because of its conveniencein obtaining micromorphology information of materials on nanometer or atomic scale under control of a computer. Based on an established SPM quantitative micromorphology analysis model, an SPM image analysis software Which can calculate both two- and three-dimensional micromorphology parameters is developed.

Research on Application of Enhanced Neural Networks in Software Risk Analysis

This paper puts forward a risk analysis model for software projects using enranced neural networks.The data for analysis are acquired through questionnaires from real software projects. To solve the multicollinearity in software risks, the method of principal components analysis is adopted in the model to enhance network stability.To solve uncertainty of the neural networks structure and the uncertainty of the initial weights, genetic algorithms is employed.The experimental result reveals that the precision of software risk analysis can be improved by using the erhanced neural networks model.

Android Apps:Static Analysis Based on Permission Classification

I IntroductionSmartphones have become more complex in terms of functions and third-party applications, and this makes lhem a living space for malware. People store private information such as accounts and passwordson their smartphones, the loss of which could have serious con- sequences.

An integrated analysis platform merging SuperDARN data within the THEMIS tool developed by ERG-Science Center (ERG-SC)

The Energization and Radiation in Geospace （ERG） mission seeks to explore the dynamics of the radiation belts in the Earth＇s inner magnetosphere with a space-borne probe （ERG satellite） in coordination with related ground observations and simulations/modeling studies. For this mission, the Science Center of the ERG project （ERG-SC） will provide a useful data analysis platform based on the THEMIS Data Analysis software Suite （TDAS）, which has been widely used by researchers in many conjunction studies of the Time History of Events and Macroscale Interactions during Substorms （THEMIS） spacecraft and ground data. To import SuperDARN data to this highly useful platform, ERG-SC, in close collaboration with SuperDARN groups, developed the Common Data Format （CDF） design suitable for fitacf data and has prepared an open database of SuperDARN data archived in CDE ERG-SC has also been developing programs written in Interactive Data Language （IDL） to load fltacf CDF files and to generate various kinds of plots-not only range-time-intensity-type plots but also two-dimensional map plots that can be superposed with other data, such as all-sky images of THEMIS-GBO and orbital footprints of various satellites. The CDF-TDAS scheme developed by ERG-SC will make it easier for researchers who are not familiar with SuperDARN data to access and analyze SuperDARN data and thereby facilitate collaborative studies with satellite data, such as the inner magnetosphere data pro- vided by the ERG （Japan）-RBSP （USA）-THEMIS （USA） fleet.

Study on the thermal environment of rural residential buildings in winter in the Guanzhong region of Shaanxi

In order to study the thermal environment of rural dwellings in cold areas,the physical environment of rural dwellings in the Guanzhong region was taken as the research object.The thermal environment parameters,such as indoor and outdoor temperature and humidity,wall surface temperature of existing dwellings were measured,recorded,and analysed using physical environment measurements and numerical software simulations.By using Ecotect Analysis software to optimize the building envelope of existing residential buildings,the thermal analysis shows that the optimized building envelope reduces the heat transfer coefficient,increases thermal insulation,reduces building energy consumption by 59%,and increases the human comfort PMV from-2.01 to-1.40.These findings provide theoretical and data support for the construction of rural dwellings in cold regions and for research into thermal environments.

Data Processing System （DPS） software with experimental design, statistical analysis and data mining developed for use in entomological research

A comprehensive but simple-to-use software package called DPS （Data Pro- cessing System） has been developed to execute a range of standard numerical analyses and operations used in experimental design, statistics and data mining. This program runs on standard Windows computers. Many of the functions are specific to entomological and other biological research and are not found in standard statistical sottware. This paper presents applications of DPS to experimental design, statistical analysis and data mining in entomology.

Computational studies on magnetism and ferroelectricity

Magnetics,ferroelectrics,and multiferroics have attracted great attentions because they are not only extremely im-portant for investigating fundamental physics,but also have important applications in information technology.Here,recent computational studies on magnetism and ferroelectricity are reviewed.We first give a brief introduction to magnets,fer-roelectrics,and multiferroics.Then,theoretical models and corresponding computational methods for investigating these materials are presented.In particular,a new method for computing the linear magnetoelectric coupling tensor without applying an external field in the first principle calculations is proposed for the first time.The functionalities of our home-made Property Analysis and Simulation Package for materials(PASP)and its applications in the field of magnetism and ferroelectricity are discussed.Finally,we summarize this review and give a perspective on possible directions of future computational studies on magnetism and ferroelectricity.

Software defect prevention based on human error theories

Software defect prevention is an important way to reduce the defect introduction rate.As the primary cause of software defects,human error can be the key to understanding and preventing software defects.This paper proposes a defect prevention approach based on human error mechanisms：DPe HE.The approach includes both knowledge and regulation training in human error prevention.Knowledge training provides programmers with explicit knowledge on why programmers commit errors,what kinds of errors tend to be committed under different circumstances,and how these errors can be prevented.Regulation training further helps programmers to promote the awareness and ability to prevent human errors through practice.The practice is facilitated by a problem solving checklist and a root cause identification checklist.This paper provides a systematic framework that integrates knowledge across disciplines,e.g.,cognitive science,software psychology and software engineering to defend against human errors in software development.Furthermore,we applied this approach in an international company at CMM Level 5 and a software development institution at CMM Level 1 in the Chinese Aviation Industry.The application cases show that the approach is feasible and effective in promoting developers＇ ability to prevent software defects,independent of process maturity levels.

A Hybrid Instance Selection Using Nearest-Neighbor for Cross-Project Defect Prediction

Software defect prediction （SDP） is an active research field in software engineering to identify defect-prone modules. Thanks to SDP, limited testing resources can be effectively allocated to defect-prone modules. Although SDP requires sufficient local data within a company, there are cases where local data are not available, e.g., pilot projects. Companies without local data can employ cross-project defect prediction （CPDP） using external data to build classifiers. The major challenge of CPDP is different distributions between training and test data. To tackle this, instances of source data similar to target data are selected to build classifiers. Software datasets have a class imbalance problem meaning the ratio of defective class to clean class is far low. It usually lowers the performance of classifiers. We propose a Hybrid Instance Selection Using Nearest-Neighbor （HISNN） method that performs a hybrid classification selectively learning local knowledge （via k-nearest neighbor） and global knowledge （via na/ve Bayes）. Instances having strong local knowledge are identified via nearest-neighbors with the same class label. Previous studies showed low PD （probability of detection） or high PF （probability of false alarm） which is impractical to overall performance as well as high PD and low PF. use. The experimental results show that HISNN produces high overall performance as well as high PD and low PF.

Identifying Composite Crosscutting Concerns with Scatter-Based Graph Clustering

Identifying composite crosscutting concerns（CCs） is a research task and challenge of aspect mining.In this paper,we propose a scatter-based graph clustering approach to identify composite CCs.Inspired by the state-of-the-art link analysis tech-niques,we propose a two-state model to approximate how CCs tangle with core modules.According to this model,we obtain scatter and centralization scores for each program element.Espe-cially,the scatter scores are adopted to select CC seeds.Further-more,to identify composite CCs,we adopt a novel similarity measurement and develop an undirected graph clustering to group these seeds.Finally,we compare it with the previous work and illustrate its effectiveness in identifying composite CCs.