FCA–CIA: An approach of using FCA to support cross-level change impact analysis for object oriented Java programs

Software change impact analysis (CIA) is a key technique to identify the potential ripple effects of the changes to software. Coarse-grained CIA techniques such as file, class and method level techniques often gain less precise change impacts, which are difficult for practical use. Fine-grained CIA techniques, such as slicing, can be used to gain more precise change impacts, but need more time and space cost. In this paper, by combining the features of the coarse-grained technique and the fine-grained technique, a variable-method (VM) correlation-based CIA technique called VM-CIA is proposed to improve the precision of static CIA. First, the VM-CIA technique uses the abstract syntax tree (AST) of program to construct a novel intermediate representation called variable and method triple (VMT), which is used to analyze the correlation between the variables and methods. Second, the VM-CIA technique proposes the single-change impact analysis algorithm and multi-change impact analysis algorithm to compute the impact set based on the VMT representation. In addition, the VM-CIA technique can get a sorted impact set which is more accurate than the existing CIA techniques. The empirical results show that the VM-CIA technique can greatly improve the precision (19%) over traditional the CIA techniques, while at the cost of a little recall (5%). Moreover, the empirical studies also show that the VM-CIA technique predicts a ranked list of potential impact results according to the distance measure, which can greatly facilitate the practical use.

Software change impact analysis (CIA) methods enable developers to understand potential impacts of a code change so that the change can be executed confidently without affecting reliability of the software. However, existing CIA approaches do not support CIA for all source code granularities. Additionally, they lack support for inter-granular change impact queries and hidden dependencies. This paper introduces Augur, an automated static code analysis-based CIA approach that addresses these shortcomings. Augur infers and maintains semantic and environment dependencies along with data and control dependencies between source code entities across granularities. Additionally, Augur uses Change Impact Query Language, a novel query language for impact analysis proposed in this paper, to support inter-granular CIA queries with batch querying feature. Augur has been realized as a Visual Studio extension called Augur-Tool. We have conducted quantitative evaluation on two open-source and two industrial projects to assess the accuracy of the tool. Results from the evaluation indicate that Augur provides CIA with high accuracy (average precision 55% and average recall 85%).

Software Requirements Changes is a typical phenomenon in any software development project. Restricting incoming changes might cause user dissatisfaction and allowing too many changes might cause delay in project delivery. Moreover, the acceptance or rejection of the change requests become challenging for software project managers when these changes are occurred in Software Development Phase. Where in Software Development Phase software artifacts are not in consistent state such as: some of the class artifacts are Fully Developed, some are Half Developed, some are Major Developed, some are Minor Developed and some are Not Developed yet. However, software effort estimation and change impact analysis are the two most common techniques which might help software project managers in accepting or rejecting change requests during Software Development Phase. The aim of this research is to develop a new software change effort estimation model which helps software project manager in estimating the effort for software Requirement Changes during Software Development Phase. Thus, this research has analyzed the existing effort estimation models and change impact analysis techniques for Softwrae Development Phase from the literature and proposed a new software change effort estimation model by combining change impact analysis technique with effort estimation model. Later, the new proposed model has been evaluated by selecting four small size software projects as case selections in applying experimental approch. The experiment results show that the overall Mean Magnitude Relative Error value produced by the new proposed model is under 25%. Hence it is concluded that the new proposed model is applicable in estimating the amount of effort for requirement changes during SDP.

When modifying a cyber-physical system, the consequences of changes need to be understood beforehand to adequately assess risks and costs. Model-based change impact analysis is key for estimating the impact of a change before actually modifying the system. Existing change impact analysis approaches apply very similar algorithms for change propagation to instances of domain-specific metamodels. However, they lack fundamental concepts for domain-spanning change impact analysis. In this paper, we propose a generic methodology for domain-spanning change impact analysis to address limitations of existing approaches. Evaluation results show the relevancy and comprehensives of the methodology for several domains.

Pattern extraction is one of the major topics in Knowledge Discovery. Out of numerous data mining techniques we propose to use a new approach: Formal Concept Analysis (FCA) together with Graph Databases with the implementation Neo4j. FCA is a prominent field of applied mathematics using maximal clusters of object-attribute relationships to discover and represent knowledge structures. The use of FCA gained much importance in many research domains in recent years. Despite the similarity of the graph representation between FCA and Neo4j, the structure and relations among the elements are represented differently and can offer a different perspective on the analysis. This paper gives more insight into how patterns can be extracted from medical data and interpreted by means of FCA and Neo4j in order to help medical staff improve the accuracy of diagnoses. We examine the factors related to patients' symptoms and diagnostics and then compare the results with the ones provided by a medical care center. We apply knowledge discovery techniques and conceptual landscapes paradigm in order to obtain an in-depth and high qualitative knowledge representation of medical data. By making use of the effectiveness and the graphical representation of conceptual hierarchies we extract valuable knowledge from medical data sets through which we solve the knowledge discovery, processing and representation task in Electronic Health Record systems.

Off to new shores: conceptual knowledge discovery and processing

In the last years, the main orientation of Formal Concept Analysis (FCA) has turned from mathematics towards computer science. This article provides a review of this new orientation and analyzes why and how FCA and computer science attracted each other. It discusses FCA as a knowledge representation formalism using five knowledge representation principles provided by Davis, Shrobe, and Szolovits [15]. It then studies how and why mathematics-based researchers got attracted by computer science. We will argue for continuing this trend by integrating the two research areas FCA and Ontology Engineering.

Change management is a critical task to control the side effects of a modification during the business process evolution. The evolution of business processes is an essential activity for the companies to better fulfill the requirements of their customers and different stakeholders. In this respect, the enterprises should adopt an effective mechanism in order to achieve the flexible business process models. It is important to identify and highlight the ripple effects of a change for minimizing their impact on other parts or entities of the system and associated services. This paper proposes a dependency-centric approach for change impact analysis. We attempt to demonstrate the change impact propagation in business process models by detecting and analyzing the interdependencies among all parts of business processes along with associated services. It can support the maintenance and evolution of business process models. The major objective is to help the modelers and business experts to assess the associated risk of intended changes and estimate the effort required for their accomplishments.

To increase and maintain web application security, developers could use some different methods, one of them is static code analysis. This method could find security vulnerabilities inside a source code without the need of running the program. It could also be automated by using tools, which considered more efficient than manual reviews. One specific method which is commonly used in static code analysis is taint analysis. Taint analysis usually utilizes source code modeling to prepare the code for analysis process to detect any untrusted data flows into security sensitives computations. While this kind of analysis could be very helpful, static code analysis tool for Laravel-based web application is still quite rare, despite its popularity. Therefore, in this research, we want to know how static code (taint) analysis could be utilized to detect security vulnerabilities and how the projects (Laravel-based) should be modeled in order to facilitate this analysis. We then developed a static analysis tool, which models the application’s source code using AST and dictionary to be used as the base of the taint analysis. The tool first parsed the route file of Laravel project to get a list of controller files. Each file in that list would be parsed in order to build the source code representation, before actually being analyzed using taint analysis method. The experiments was done using this tool shows that the tools (with taint analysis) could detect 13 security vulnerabilities from 6 Laravel-based projects with one False Negative. An ineffective sanitizer was the suspected cause of this False Negative. This also shows that proposed modeling technique could be helpful in facilitating taint analysis in Laravel-based projects. For future development and studies, this tool should be tested with more Laravel and even other framework based web application with a wider range of security vulnerabilities.

The large number of industrial control systems based on PLCs (Programmable Logic Controllers) available at CERN implies a huge number of programs and lines of code. The software quality assurance becomes a key point to ensure the reliability of the control systems. Static code analysis is a relatively easy-to-use, simple way to find potential faults or error-prone parts in the source code. While static code analysis is widely used for general purpose programming languages (e.g. Java, C), this is not the case for PLC program languages. We have analyzed the possibilities and the gains to be expected from applying static analysis to the PLC code used at CERN, based on the UNICOS framework. This paper reports on our experience with the method and the available tools and sketches an outline for future work to make this analysis method practically applicable.

The digital medium allows visitors, curators and art historians to gain new insights into their collections through data analysis and rich, interactive visualizations. Motivated by the rise of large-scale cultural heritage collections that have emerged on the Web, we argue that Formal Concept Analysis can be used to highlight the relationships between objects and their features within digital art collections and provide a means for visitors to explore these collections via interactive, narrated pathways. Our work presents four research projects that span 10 years from 2005 - 2015 – ImageSleuth, The Virtual Museum of the Pacific, A Place for Art and a scalability study of Formal Concept Analysis as applied to a data-set from the Brooklyn Museum. Our approach is based on the idea that much of the meaning that can be interpreted from museum collections lies – at least in part – in the way that objects are related to one another. Our work examines how Formal Concept Analysis can drive explorative, narrative-based visitor experiences and reveal new insights into cultural heritage collections.

The paper first offers a parallel between two approaches to conceptual clustering, namely formal concept analysis (augmented with the introduction of new operators) and bipartite graph analysis. It is shown that a formal concept (as defined in formal concept analysis) corresponds to the idea of a maximal bi-clique, while sub-contexts, which correspond to independent “conceptual worlds” that can be characterized by means of the new operators introduced, are disconnected sub-graphs in a bipartite graph. The parallel between formal concept analysis and bipartite graph analysis is further exploited by considering “approximation” methods on both sides. It leads to suggest new ideas for providing simplified views of datasets, taking also inspiration from the search for approximate itemsets in data mining (with relaxed requirements), and the detection of communities in hierarchical small worlds.

Evaluation of IPAQ questionnaires supported by formal concept analysis

With the exponential growth of the World Wide Web (WWW), it has become the most popular place to gather information. However the size of the WWW makes it difficult for people to locate relevant information. About 85% of all Web users use search engines of some kind for this purpose. However, existing search engines often do not return relevant information. The main focus of this paper is to improve search performance by using keywords and web pages which have been previously used or visited by other users. The Formal Concept Analysis (FCA) method has been adapted to maintain a concept map of keywords. This paper shows how both precision and recall has improved the specific-domain area, in which users can share the same knowledge.

XML (eXtensible Markup Language) documents are the main format for publishing and interchanging data on the Web. Integrity constraints are essential in data design. Functional dependencies are the most important semantic constraints. Functional dependencies satisfied by XML data have been introduced recently. Formal Concept Analysis (FCA) is a mathematical theory of concept hierarchies which is based on Lattice Theory. Data is represented as a two-dimensional context of objects and attributes. FCA discovers dependencies within the data based on the relation among objects and attributes. In this paper we take a first step towards using an FCA approach to study functional dependencies in XML databases. The novelty of our approach is the software, which analyzes an XML document, constructs the Formal Context corresponding to the flat representation of the XML data and finds the implications, which are functional dependencies in XML data.