International Journal of Software Engineering and Knowledge Engineering

The objective of this paper is statistical comparison of modelling methods for software maintainability prediction. The statistical comparison is performed by building software maintainability prediction models using 27 different regression and machine learning based algorithms. For this purpose, software metrics datasets of two different commercial object-oriented systems are used. These systems were developed using an object oriented programming language Ada. These systems are User Interface Management System (UIMS) and Quality Evaluation System (QUES). It is shown that different measures like MMRE, RMSE, Pred(0.25) and Pred(0.30) calculated on predicted values obtained from leave one out (LOO) cross validation produce very divergent results regarding accuracy of modelling methods. Therefore the 27 modelling methods are evaluated on the basis of statistical significance tests. The Friedman test is used to rank various modelling methods in terms of absolute residual error. Six out of the ten top ranked modelling methods are common to both UIMS and QUES. This indicates that modelling methods for software maintainability predicton are solid and scalable. After obtaining ranks, pair wise Wilcoxon Signed rank test is performed. Wilcoxon Sign rank test indicates that the top ranking method in UIMS data set is significantly superior to only four other modelling methods whereas the top tanking method in QUES data set is significantly superior to 11 other modelling methods. The performance of instance based learning algorithms — IBk and Kstar is comparable to modelling methods used in earlier studies.

As software systems become more and more complex, there is need to consider not only data structures and algorithms but also the general structure or architecture of the system. Many researchers have presently focused on dynamic evolution of software architectures. Most of them usually emphasized on describing and analyzing the dynamic evolution process of software architectures, while lacking formally modeling and verifying composite dynamic evolution of software architectures. In this paper, we propose a formal method of modeling and verifying composite dynamic evolution of software architectures using hypergraph grammars. We represent software architectures with hypergraphs, give out corresponding composite evolution rules of software architectures, and then model composite dynamic evolution of software architectures according to those rules. At last we verify the liveness property of composite dynamic evolution of software architectures using model checking, and give out corresponding verification algorithms. Our approach provides a graphical representation for composite dynamic evolution of software architectures, and displays a formal theoretical basis on grammars.

The design and implementation of a programming environment including an editor, a debugger and an interpreter engine for Lograph, a general-purpose visual logic programming language, is discussed. The rationale for user-interface design decisions is presented, the goal of which is to increase cognitive support for the creation, exploration and debugging of Lograph programs. The design of the interpreter engine allows for animation of execution in the debugger. The engine takes full advantage of an efficient implementation of Prolog, and operates on a Prolog translation of Lograph programs and queries. The translated Lograph programs are probed with instrumentation code at appropriate places so that applications of Lograph rules are reported to the visual interface of the Lograph debugger as a side effect of the execution of a program.

Each rational investment decision, including those made by clients with regard to Business Software Systems (BSS) Development and Enhancement Projects (D&EP), should meet two measurable criteria: effectiveness and economic efficiency. However, in the case of BSS D&EP, the assumption concerning the measurability of these criteria is often treated as controversial. Meanwhile, the so-called concept of BSS D&EP Functional Assessment (FA), proposed by the author and verified in practice, allows for using the potential offered by the Functional Size Measurement (FSM) concept and methods in the area of the quantitative evaluation of these two criteria. Thus, the paper aims at presenting and proving the capabilities of using the functional assessment concept in the quantitative evaluation of BSS D&EP effectiveness and economic efficiency. Linking FSM issues with managerial aspects through the FA concept may contribute to a better understanding of the FSM importance, which is still underestimated by business managers, as in the subject literature this issue is usually considered from a technical perspective. Meanwhile, BSS D&EP FA can constitute the basis for rational decisions not only for BSS providers, but also for BSS clients. These issues classify into economics problems of software engineering research and practice.

This article presents a novel crawling and clustering method for extracting and processing cultural data from the web in a fully automated fashion. Our architecture relies upon a focused web crawler to download web documents relevant to culture. The focused crawler is a web crawler that searches and processes only those web pages that are relevant to a particular topic. After downloading the pages, we extract from each document a number of words for each thematic cultural area, filtering the documents with non-cultural content; we then create multidimensional document vectors comprising the most frequent cultural term occurrences. We calculate the dissimilarity between the cultural-related document vectors and for each cultural theme, we use cluster analysis to partition the documents into a number of clusters. Our approach is validated via a proof-of-concept application which analyzes hundreds of web pages spanning different cultural thematic areas.

Big data and big data analysis are a multi-dimensional scientific and technological pursuit that has profound impact on the society as a whole. Though big data has become such a catchy buzzword, to make any significant stride in this pursuit, we must have a clear picture of what big data is and what big data analysis entails. In this paper, after a brief account on the landscape of big data and big data analysis, we focus attention on two issues: granularities of knowledge content in big data, and utility of inconsistencies in big data analysis.