Vol. 35 · No. 3

Software cognitive complexity refers to how demanding the mental process of performing tasks such as coding, testing, debugging, or modifying source code is. Achieving low levels of cognitive complexity is crucial for ensuring high levels of software maintainability, which is one of the most rewardful software quality attributes. Therefore, in order to control and ensure software maintainability, it is first necessary to accurately quantify software cognitive complexity. In this line, this paper presents a software metric to assess cognitive complexity in Object-Oriented (OO) systems, and particularly those developed in the Java language, which is very popular among OO programming languages. The proposed metric is based on a characterization of basic control structures present in Java systems. Several algorithms to compute the metric and their materialization in the Eclipse IDE are also introduced. Finally, a theoretical validation of the metric against a framework specially designed to validate software complexity metrics is presented, and the applicability of the tool is shown by illustrating the metric in the context of ten real world Java projects and relevant metrics from the well-known Chidamber-Kemerer metric suite.

The paper describes some practical aspects of using LISp-Miner for data mining. LISp-Miner is a software tool that is under development at the University of Economics, Prague. We will review the different types of knowledge patterns discovered by the system, and discuss their applicability for various data mining tasks. We also compare LISp-Miner 18.16 with Weka 3.6.9 and Rapid Miner 5.3.

A known way to improve the accuracy of dependency parsers is to combine several different parsing algorithms, in such a way that the weaknesses of each of the models can be compensated by the strengths of others. For example, voting-based combination schemes are based on variants of the idea of analyzing each sentence with various parsers, and constructing a combined output where the head of each node is determined by ``majority vote'' among the different parsers. Typically, such approaches combine very different parsing models to take advantage of the variability in the parsing errors they make. In this paper, we show that consistent improvements in accuracy can be obtained in a much simpler way by combining a single parser with itself. In particular, we start with a greedy implementation of the Nivre pseudo-projective arc-eager algorithm, a well-known left-to-right transition-based parser, and we combine it with a ``mirrored'' version of the algorithm that analyzes sentences from right to left. To determine which of the two obtained outputs we trust for the head of each node, we use simple criteria based on the length and position of dependency arcs. Experiments on several datasets from the CoNLL-X shared task and the WSJ section of the English Penn Treebank show that the novel combination system obtains better performance than the baseline arc-eager parser in all cases. To test the generality of the approach, we also perform experiments with a different transition system (arc-standard) and a different search strategy (beam search), obtaining similar improvements in all these settings.

The data de-duplication system not only pursues the high de-duplication rate, which refers to the aggregate reduction in storage requirements gained from de-duplication, but also the de-duplication speed. To solve the problem of random parameter-setting brought by Content Defined Chunking (CDC), a self-adaptive data chunking algorithm is proposed. The algorithm improves the de-duplication rate by conducting pre-processing de-duplication to the samples of the classified files and then selecting the appropriate algorithm parameters. Meanwhile, FastCDC, a kind of content-based fast data chunking algorithm, is adopted to solve the problem of low de-duplication speed of CDC. By introducing de-duplication factor and acceleration factor, FastCDC can significantly boost de-duplication speed while not sacrificing the de-duplication rate through adjusting these two parameters. The experimental results demonstrate that our proposed method can improve the de-duplication rate by about 5

Linear Discriminant Analysis (LDA) and the related Fisher's linear discriminant are very important techniques used for classification and for dimensionality reduction. A certain complication occurs in applying these methods to real data. We have to estimate the class means and common covariance matrix, which are not known. A problem arises if the number of features exceeds the number of observations. In this case the estimate of the covariance matrix does not have full rank, and so cannot be inverted. There are a number of ways to deal with this problem. In our previous paper, we proposed improving LDA in this area, and we presented a new approach which uses a generalization of the Moore–Penrose (MP) pseudo-inverse to remove this weakness. However, for data sets with a larger number of features, our method was computationally too slow to achieve good results. Now we propose a model selection method with a genetic algorithm to solve this problem. Experimental results on different data sets demonstrate that the improvement is efficient.

The objective of Maximal Covering Location Problem is locating facilities such that they cover the maximal number of locations in a given radius or travel time. MCLP is applied in many different real-world problems with several modifications. In this paper a new model of MCLP with fuzzy conditions is presented. It uses two types of fuzzy numbers for describing two main parameters of MCLP – coverage radius and distances between locations. First, the model is defined, then Particle Swarm Optimization method for solving the problem is described and tested.

Reflection, reflection-based programming and metaprogramming are valuable tools for many programming tasks, like the implementation of persistence and serialization-like operations, object-relational mapping, remote method invocation, automatic generation of user-interfaces, etc., and also for the implementation of several design patterns. C++ as one of the most prevalent programming languages still lacks support for standardised, compiler-assisted reflection. In this paper we introduce in short the Mirror reflection library which is a part of an ongoing effort to add reflection to C++ and we will show how reflection can be used to greatly simplify the implementation of object factories – classes constructing instances of other classes from various external data representations.

In this article we present a simulator of non-deterministic static P systems using Field Programmable Gate Array (FPGA) technology. Its major feature is a high performance, achieving a constant processing time for each transition. Our approach is based on representing all possible applications as words of some regular context-free language. Then, using formal power series it is possible to obtain the number of possibilities and select one of them following a uniform distribution, in a fair and non-deterministic way. According to these ideas, we yield an implementation whose results show an important speed-up, with a strong independence from the size of the P system.

The main purpose of this paper is to present a new formal definition that can be used for modeling functional test. Initially, WorkFlow nets are used to represent the main functional requirement of the software. Next, Object WorkFlow nets derived from WorkFlow nets and object Petri nets are used to formally specify the test models of object oriented software functionalities to be used. In particular, the proposed models allow for the addition of complex data structure specifications as well as complex control specifications. The dynamic execution of functional testing models, when considering a specific software architecture, is given by the instantiation of a testing class associated with the tested functionality. An example of execution of functional testing corresponding to the ``Withdrawal Operation'' of a bank ATM machine is presented, as well as a comparative study based on a more traditional UML modeling approach.

According to the increment of accessible text data source on the internet, it has increased the necessity of the automatic text document summarization. However, the performance of the automatic methods might be poor because the semantic gap between high level user's summary requirement and low level vector representation of machine exists. In this paper, to overcome that problem, we propose a new document summarization method using a pseudo relevance feedback based on clustering method and NMF (non-negative matrix factorization). Relevance feedback is effective technique to minimize the semantic gap of information processing, but the general relevance feedback needs an intervention of a user. Additionally, the refined query without user interference by pseudo relevance feedback may be biased. The proposed method provides an automatic relevance judgment to reformulate query using the clustering method for minimizing a bias of query expansion. The method also can improve the quality of document summarization since the summarized documents are influenced by the semantic features of documents and the expanded query. The experimental results demonstrate that the proposed method achieves better performance than the other document summarization methods.