An experimental comparison of usage-based and checklist-based reading

Software quality can be defined as the customers' perception of how a system works. Inspection is a method to monitor and control the quality throughout the development cycle. Reading techniques applied to inspections help reviewers to stay focused on the important parts of an artifact when inspecting. However, many reading techniques focus on finding as many faults as possible, regardless of their importance. Usage-based reading helps reviewers to focus on the most important parts of a software artifact from a user's point of view. We present an experiment, which compares usage-based and checklist-based reading. The results show that reviewers applying usage-based reading are more efficient and effective in detecting the most critical faults from a user's point of view than reviewers using checklist-based reading. Usage-based reading may be preferable for software organizations that utilize or start utilizing use cases in their software development.     

A replicated quasi-experimental study on the influence of personality and team climate in software development

This article reports a replication of a quasi-experimental study analyzing how personality factors and team climate influence software development team effectiveness, product quality and team member satisfaction. The replication was designed on the basis of the original quasi-experimental study, both of which were run in an academic setting. In the original study, data were collected from a sample of 35 three-member developer teams. All these teams used an adaptation of extreme programming (XP) to the academic environment to develop the same software system. In the replication, the data were collected from a sample of 34 three- or four-member developer teams working on the same software project. Student teams used a common object-oriented software development paradigm to solve the set problem and applied the Unified Process. In both studies all teams were formed at random, and their members were blind to the quasi-experimental conditions and hypotheses. The replication of this empirical study aims to verify the results of the original quasi-experiment. It examines, first, whether personality factors (neuroticism, extroversion, agreeableness, openness to experience and conscientiousness) are related to the quality of the developed software and team member satisfaction and, second, whether the preferences, perceptions and preferences-perceptions fit for the four team climate factors (participative safety, support for innovation, team vision and task orientation) are related to the quality of the developed software and team member satisfaction. The results of the replication corroborate some of the findings of the original study. On the one hand, the results revealed that there is a significant correlation between the extroversion personality factor and software quality, but no significant correlation between the extroversion personality factor and team satisfaction. Also, we found that the perception of team climate where participative safety is high is related to better quality software. We observed significant relationships between the perception of the four team climate factors and team member satisfaction. Additionally, the results showed a positive relationship between software quality and teams in which the real climate perception at the end of the project is better than preferences stated by team members at the outset of the project for the participative safety factor. Finally, we found that teams where the real climate is better than the stated preferences for the team orientation factor exhibit a direct and positive relationship to team member satisfaction.     

Using semantic components to search for domain-specific documents: An evaluation from the system perspective and the user perspective

We seek to leverage an expert user's knowledge about how information is organized in a domain and how information is presented in typical documents within a particular domain-specific collection, to effectively and efficiently meet the expert's targeted information needs. We have developed the semantic components model to describe important semantic content within documents. The semantic components model for a given collection (based on a general understanding of the type of information needs expected) consists of a set of document classes, where each class has an associated set of semantic components. Each semantic component instance consists of segments of text about a particular aspect of the main topic of the document and may not correspond to structural elements in the document. The semantic components model represents document content in a manner that is complementary to full text and keyword indexing. This paper describes how the semantic components model can be used to improve an information retrieval system. We present experimental evidence from a large interactive searching study that compared the use of semantic components in a system with full text and keyword indexing, where we extended the query language to allow users to search using semantic components, to a base system that did not have semantic components. We evaluate the systems from a system perspective, where semantic components were shown to improve document ranking for precision-oriented searches, and from a user perspective. We also evaluate the systems from a session-based perspective, evaluating not only the results of individual queries but also the results of multiple queries during a single interactive query session.     

An automatic classification of text documents based on correlative association of words

Training speed of the classifier without degrading its predictive capability is an important concern in text classification. Feature selection plays a key role in this context. It selects a subset of most informative words (terms) from the set of all words. The correlative association of words towards the classes increases an incertitude for the words to represent a class. The representative words of a class are either of positive or negative nature. The standard feature selection methods, viz. Mutual Information (MI), Information Gain (IG), Discriminating Feature Selection (DFS) and Chi Square (CHI), do not consider positive and negative nature of the words that affects the performance of the classifiers. To address this issue, this paper presents a novel feature selection method named Correlative Association Score (CAS). It combines the strength, mutual information, and strong association of the words to determine their positive and negative nature for a class. CAS selects a few (k) informative words from the set of all words (m). These informative words generate a set of N-grams of length 1-3. Finally, the standard Apriori algorithm ensembles the power of CAS and CHI to select the top most, b informative N-grams, where b is a number set by an empirical evaluation. Multinomial Naive Bayes (MNB) and Linear Support Vector Machine (LSVM) classifiers evaluate the performance of the selected N-Grams. Four standard text data sets, viz. Webkb, 20Newsgroup, Ohsumed10, and Ohsumed23 are used for experimental analysis. Two standard performance measures named Macro_F1 and Micro_F1 show a significant improvement in the results using proposed CAS method.     

An efficient image authentication method based on Hamming code

Image authentication has come through a history of several years. However, up to the present time, most mainstream image authentication schemes are still unable to detect burst bit errors. Moreover, the capability of recovering tampered pixels in detail (complex) areas has not been very satisfactory either. In this paper, we offer to combine the Hamming code technique, Torus automorphism and bit rotation technique to do tamper proofing. According to our experimental results, our new hybrid method can effectively eliminate burst bit errors, and our recovered pixels in detail areas can actually gain very high clarity. The results show that our scheme is quite a practical method, which is quite able to detect and recover tampered areas.     

Code churn estimation using organisational and code metrics: An experimental comparison

Context

Source code revision control systems contain vast amounts of data that can be exploited for various purposes. For example, the data can be used as a base for estimating future code maintenance effort in order to plan software maintenance activities. Previous work has extensively studied the use of metrics extracted from object-oriented source code to estimate future coding effort. In comparison, the use of other types of metrics for this purpose has received significantly less attention.

Objective

This paper applies machine learning techniques to unveil predictors of yearly cumulative code churn of software projects on the basis of metrics extracted from revision control systems.

Method

The study is based on a collection of object-oriented code metrics, XML code metrics, and organisational metrics. Several models are constructed with different subsets of these metrics. The predictive power of these models is analysed based on a dataset extracted from eight open-source projects.

Results

The study shows that a code churn estimation model built purely with organisational metrics is superior to one built purely with code metrics. However, a combined model provides the highest predictive power.

Conclusion

The results suggest that code metrics in general, and XML metrics in particular, are complementary to organisational metrics for the purpose of estimating code churn.     

A replicated assessment and comparison of adaptation techniques for analogy-based effort estimation

Variants of adaptation techniques have been proposed in previous studies to improve the performance of analogy-based effort estimation. The results of these studies are often contradictory and cannot simply be generalized because there are many uncontrollable source of variations between adaptation studies. The study presented in this paper has been carried out in order to replicate the assessment and comparison of different adaptation techniques utilised in analogy-based software effort prediction. Empirical evaluation of variants of adaptation techniques with Jack-knifing procedure have been carried out. Seven datasets come from PROMISE data repository were used for benchmarking. The results are also investigated within the presence/absence of feature subset selection algorithm. The current study permitted us to discover that linear adjustment approaches are more accurate than nonlinear adjustment because of the nature of the employed datasets that have, in most cases, normality characteristics.     

Client-side selection of replicated web services: An empirical assessment

Replicating web services over physically distributed servers can offer client applications a number of QoS benefits, including higher availability and reduced response time. However, selecting the “best” service replica to invoke at the client-side is not a trivial task, as this requires taking into account factors such as local and external network conditions, and the servers’ current workload. This paper presents an empirical assessment of five representative client-side service selection policies for accessing replicated web services. The assessment measured the response time obtained with each of the five policies, at two different client configurations, when accessing a world-wide replicated service with four replicas located in three continents. The assessment’s results were analyzed both quantitatively and qualitatively. In essence, the results show that, in addition to the QoS levels provided by the external network and the remote servers, characteristics of the local client environment can have a significant impact on the performance of some of the policies investigated. In this regard, the paper presents a set of guidelines to help application developers in identifying a server selection policy that best suits a particular service replication scenario.     

An adaptive quality of service aware middleware for replicated services

A dependable middleware should be able to adaptively share the distributed resources it manages in order to meet diverse application requirements, even when the quality of service (QoS) is degraded due to uncertain variations in load and unanticipated failures. We have addressed this issue in the context of a dependable middleware that adaptively manages replicated servers to deliver a timely and consistent response to time-sensitive client applications. These applications have specific temporal and consistency requirements, and can tolerate a certain degree of relaxed consistency in exchange for better response time. We propose a flexible QoS model that allows clients to specify their timeliness and consistency constraints. We also propose an adaptive framework that dynamically selects replicas to service a client's request based on the prediction made by probabilistic models. These models use the feedback from online performance monitoring of the replicas to provide probabilistic guarantees for meeting a client's QoS specification. The experimental results we have obtained demonstrate the role of feedback and the efficacy of simple analytical models for adaptively sharing the available replicas among the users under different workload scenarios.     

An application for plagiarized source code detection based on a parse tree kernel

Program plagiarism detection is a task of detecting plagiarized code pairs among a set of source codes. In this paper, we propose a code plagiarism detection system that uses a parse tree kernel. Our parse tree kernel calculates a similarity value between two source codes in terms of their parse tree similarity. Since parse trees contain the essential syntactic structure of source codes, the system effectively handles structural information. The contributions of this paper are two-fold. First, we propose a parse tree kernel that is optimized for program source code. The evaluation shows that our system based on this kernel outperforms well-known baseline systems. Second, we collected a large number of real-world Java source codes from a university programming class. This test set was manually analyzed and tagged by two independent human annotators to mark plagiarized codes. It can be used to evaluate the performance of various detection systems in real-world environments. The experiments with the test set show that the performance of our plagiarism detection system reaches to 93% level of human annotators.     

An evaluation of medium-grain dataflow code

In this paper, we study several issues related to the medium grain dataflow model of execution. We present bottom-up compilation of medium grainclusters from a fine grain dataflow graph. We compare thebasic block and thedependence sets algorithms that partition dataflow graphs into clusters. For an extensive set of benchmarks we assess the average number of instructions in a cluster and the reduction in matching operations compared with fine grain dataflow execution. We study the performance of medium grain dataflow when several architectural parameters, such as the number of processors, matching cost, and network latency, are varied. The results indicate that medium grain execution offers a good speedup over the fine grain model, that it is scalable, and tolerates network latency and high matching costs well. Medium grain execution can benefit from a higher output bandwidth of a processor and fainally, a simple superscalar processor with an issue rate of two is sufficient to exploit the internal parallelism of a cluster. This work is supported in part by NSF Grants CCR-9010240 and MIP-9113268.     

基于XML文档相似性的构件聚类分析

构件聚类时,提出了一种计算基于XML描述的构件问相似度的递归算法,能有效度量构件XML描述文档包含的结构和语义信息.构造文档相似矩阵,利用遗传算法将高维样本映射到二维平面上,使用k-means算法聚类,获得全局最优的构件聚类.最后,在构件库测试模型上进行实验,实验结果表明,基于XML相似度的构件聚类算法在构件查询实践中具有可行性和有效性.     

一种基于HADOOP的办公文件存储方法

HADOOP分布式文件系统为解决分布式存储提供了良好的平台。为了良好的管理办公系统中的日常办公文件,针对办公文件占用空间小而数量多而导致的体积庞大的特点,系统应用HADOOP分布式文件系统,为了减少命名节点的内存过大,提出了使用二进制序列化合并小文件的方案,并设置合并器,采取闲时合并,忙时存在本地降低写操作时间。并根据文件的属性分类归并写入,可以在查询时避免不必要的遍历而降低速度,提高检索性能。通过测试文件合并时间、文件上传时间以及文件查找读取时间,本方法可以很好的处理办公文件的上传与读取,解决办公文件的存储问题。     

Algorithm for segmentation of documents based on texture features

The ascending approach to segmentation of scanned documents in the area of background, text, and photographs is considered. In the first stage, the image is divided into blocks. For each block, a series of texture features is calculated. On the basis of these features, the type of the block is determined. Various positions and sizes of blocks, 26 texture features, and 4 algorithms of classification of blocks were considered. In the second stage, the type of block was corrected on the basis of the analysis of neighboring regions. For estimating the results, the error matrix and the ICDAR 2007 criterion are used.     

Ink analysis based forensic investigation of handwritten legal documents

Document falsification is among the fastest growing problems all over the world. Disclosure of such document is not always possible due to the conspiracy of attorney bodies; especially legal documents such as bank cheques, contracts, cash memos, and so on. Handwritten document tampering detection due to addition of new word(s) in judicial documents is the prime objective of this research. Minute alteration in writing causes financial loss to a person or to an organization and decreases the global economy. Such intangible assets remain undiscovered owing to lack of proper forensic techniques. Though writing style imitation can be possible, however, the possibility of getting exactly the same pen of the authorized document is quite impossible for an imitator. Hence, the paper introduces a solution to detect forgery in handwritten legal documents by analyzing perceptually similar pen ink. Forgery activity happens either ends of a written document by appending new word(s)/letter(s) with similar type of pen. The work is formulated as a binary classification problem and established with the help of several statistical features and three different classifiers: Multilayer Perceptron(MLP), RBF-SVM, and Random Forest(RF). Besides, the problem has also been implemented through some DCNN approaches to check whether it is possible to reflect the forgery by direct approaches. The efficiency of the proposed method is quite promising for involvement in the examination of forensic documents.

     

An approach based on the domain perspective to develop WSAN applications

As wireless sensor and actuator networks (WSANs) can be used in many different domains, WSAN applications have to be built from two viewpoints: domain and network. These different viewpoints create a gap between the abstractions handled by the application developers, namely the domain and network experts. Furthermore, there is a coupling between the application logic and the underlying sensor platform, which results in platform-dependent projects and source codes difficult to maintain, modify, and reuse. Consequently, the process of developing an application becomes cumbersome. In this paper, we propose a model-driven architecture (MDA) approach for WSAN application development. Our approach aims to facilitate the task of the developers by: (1) enabling application design through high abstraction level models; (2) providing a specific methodology for developing WSAN applications; and (3) offering an MDA infrastructure composed of PIM, PSM, and transformation programs to support this process. Our approach allows the direct contribution of domain experts in the development of WSAN applications, without requiring specific knowledge of programming WSAN platforms. In addition, it allows network experts to focus on the specific characteristics of their area of expertise without the need of knowing each specific application domain.     

An empirical study of sentiment analysis for chinese documents

Up to now, there are very few researches conducted on sentiment classification for Chinese documents. In order to remedy this deficiency, this paper presents an empirical study of sentiment categorization on Chinese documents. Four feature selection methods (MI, IG, CHI and DF) and five learning methods (centroid classifier, K-nearest neighbor, winnow classifier, Naïve Bayes and SVM) are investigated on a Chinese sentiment corpus with a size of 1021 documents. The experimental results indicate that IG performs the best for sentimental terms selection and SVM exhibits the best performance for sentiment classification. Furthermore, we found that sentiment classifiers are severely dependent on domains or topics.     

基于中值滤波和形态学的条码边缘检测

针对边缘模糊会导致二维条码识别率下降的问题,提出了一种基于中值滤波和数学形态学相结合的二维条码边缘检测识别算法,该算法能够有效地抑制噪声,保护边缘细节,提高检测精度,从而有效地降低边缘模糊时条码识别的影响.文中选取PDF417二维条码为应用对象,与传统的几种边缘检测算法相比.实验结果表明,该算法能够更有效地识别条码边界,显著地提高了条码的识别率.     

An implementation of granular dynamics for simulating frictional elastic particles based on the DL_POLY code

We have modified Daresbury Laboratory's replicated data strategy (RDS) parallel molecular dynamics (MD) package DL_POLY (version 2.13) to study the granular dynamics of frictional elastic particles. DL_POLY [Smith and Forester, The DL_POLY_2 User Manual v2.13, 2001; Forester and Smith, The DL_POLY_2 Reference Manual v2.13, 2001] is a MD package originally developed to study liquid state and macromolecular systems by accounting for various molecular interaction forces. The particles of interest in this study are macroscopic grains in pharmaceutical powders, with sizes ranging from tens to hundreds of microns. We have therefore substituted the molecular interaction forces with contact forces (including linear-dashpot, HKK interaction forces and Coulombic friction) while taking advantage of the RDS scheme. In effect, we have created a parallel Discrete Element Simulation (DES) code. In this paper, we describe the modifications made to the original DL_POLY code and the results from the validation tests of the granular dynamics simulations for systems of monodisperse spherical particles settling under gravity. The code can also be utilized to study particle packings generated via uniaxial compaction and, in some cases, simultaneous application of shear, at constant strain.     

An implementation of a replicated file server supporting the crash-recovery failure model

Data replication techniques are widely used for improving availability in software applications. Replicated systems have traditionally assumed the fail-stop model, which limits fault tolerance. For this reason, there is a strong motivation to adopt the crash-recovery model, in which replicas can dynamically leave and join the system. With the aim to point out some key issues that must be considered when dealing with replication and recovery, we have implemented a replicated file server that satisfies the crash-recovery model, making use of a Group Communication System. According to our experiments, the most interesting results are that the type of replication and the number of replicas must be carefully determined, specially in update intensive scenarios; and, the variable overhead imposed by the recovery protocol to the system. From the latter, it would be convenient to adjust the desired trade-off between recovery time and system throughput in terms of the service state size and the number of missed operations.