Test data generation from UML state machine diagrams using GAs

Automatic test data generation helps testers to validate software against user requirements more easily. Test data can be generated from many sources; for example, experience of testers, source program, or software specification. Selecting a proper test data set is a decision making task. Testers have to decide what test data that they should use, and a heuristic technique is needed to solve this problem automatically. In this paper, we propose a framework for generating test data from software specifications. The selected specification is Unified Modeling Language (UML) state machine diagram. UML state machine diagram describes a system in term of state which can be changed when there is an action occurring in the system. The generated test data is a sequence of these actions. These sequences of action help testers to know how they should test the system. The quality of generated test data is measured by the number of transitions which is fired using the test data. The more transitions test data can fire, the better quality of test data is. The number of coverage transitions is also used as a feedback for a heuristic search for a better test set. Genetic algorithms (GAs) are selected for searching the best test data. Our experimental results show that the proposed GA-based approach can work well for generating test data for some types of UML state machine diagrams

Partial Reconfiguration in the Field of Logic Controllers Design

The paper presents method for logic controllers multi context implementation by means of partial reconfiguration. The UML state machine diagram specifies the behaviour of the logic controller. Multi context functionality is specified at the specification level as variants of the composite state. Each composite state, both orthogonal or compositional, describes specific functional requirement of the control process. The functional decomposition provided by composite states is required by the dynamic partial reconfiguration flow. The state machines specified by UML state machine diagrams are transformed into hierarchical configurable Petri nets (HCfgPN). HCfgPN are a Petri nets variant with the direct support of the exceptions handling mechanism. The paper presents placesoriented method for HCfgPN description in Verilog language. In the paper proposed methodology was illustrated by means of simple industrial control process

Teaching Concurrent Software Design: A Case Study Using Android

In this article, we explore various parallel and distributed computing topics from a user-centric software engineering perspective. Specifically, in the context of mobile application development, we study the basic building blocks of interactive applications in the form of events, timers, and asynchronous activities, along with related software modeling, architecture, and design topics.Comment: Submitted to CDER NSF/IEEE-TCPP Curriculum Initiative on Parallel and Distributed Computing - Core Topics for Undergraduate

Graph Digitalizer

Szükséges lehet kinyerni tudományos cikkek ábráiból, mért regisztrátumokból egy függvény grafikonjának (x, y) pontjait, de előfordulhat, hogy nincs mód megszerezni az eredeti adatokat. Pontok koordinátáit, oszlopdiagramok magasságát egyszerűen le lehet olvasni, de folytonos görbéket nem leeht szemmel digitalizálni. A program feladata beszkennelt PDF-ből kivágott grafikonok, mit képfájlok betöltése, binárissá alakítása, simitása, 1 pixelessé vékonyítása (szkelotonizálása) és kalibrálása. Tegye lehetővé az így kinyert függvénypontok fájlba mentését. A program rendelkezzen felhasználói felülettel, nyelve C++

Modelling learning behaviour of intelligent agents using UML 2.0

This thesis aims to explore and demonstrate the ability of the new standard of structural and behavioural components in Unified Modelling Language (UML 2.0 / 2004) to model the learning behaviour of Intelligent Agents. The thesis adopts the research direction that views agent-oriented systems as an extension to object-oriented systems. In view of the fact that UML has been the de facto standard for modelling object-oriented systems, this thesis concentrates on exploring such modelling potential with Intelligent Agent-oriented systems. Intelligent Agents are Agents that have the capability to learn and reach agreement with other Agents or users. The research focuses on modelling the learning behaviour of a single Intelligent Agent, as it is the core of multi-agent systems. During the writing of the thesis, the only work done to use UML 2.0 to model structural components of Agents was from the Foundation for Intelligent Physical Agent (FIPA). The research builds upon, explores, and utilises this work and provides further development to model the structural components of learning behaviour of Intelligent Agents. The research also shows the ability of UML version 2.0 behaviour diagrams, namely activity diagrams and sequence diagrams, to model the learning behaviour of Intelligent Agents that use learning from observation and discovery as well as learning from examples of strategies. The research also evaluates if UML 2.0 state machine diagrams can model specific reinforcement learning algorithms, namely dynamic programming, Monte Carlo, and temporal difference algorithms. The thesis includes user guides of UML 2.0 activity, sequence, and state machine diagrams to allow researchers in agent-oriented systems to use the UML 2.0 diagrams in modelling the learning components of Intelligent Agents. The capacity for learning is a crucial feature of Intelligent Agents. The research identifies different learning components required to model the learning behaviour of Intelligent Agents such as learning goals, learning strategies, and learning feedback methods. In recent years, the Agent-oriented research has been geared towards the agency dimension of Intelligent Agents. Thus, there is a need to conduct more research on the intelligence dimension of Intelligent Agents, such as negotiation and argumentation skills. The research shows that behavioural components of UML 2.0 are capable of modelling the learning behaviour of Intelligent Agents while structural components of UML 2.0 need extension to cover structural requirements of Agents and Intelligent Agents. UML 2.0 has an extension mechanism to fulfil Agents and Intelligent Agents for such requirements. This thesis will lead to increasing interest in the intelligence dimension rather than the agency dimension of Intelligent Agents, and pave the way for objectoriented methodologies to shift more easily to paradigms of Intelligent Agent-oriented systems.The British Council, the University of Plymouth and the Arab-British Chamber Charitable Foundation

Refactoring of UML models using AGG

Model refactoring is an emerging research topic that is heavily inspired by refactoring of object-oriented programs. Current-day UML modeling environments provide poor support for evolving UML models and applying refactoring techniques at model level. As UML models are intrinsically graph-based in nature we propose to use graph transformations to specify and apply model refactoring. More in particular, we use a specific graph transformation tool, AGG, and provide recommendations of how AGG may be improved to better support model refactoring. These recommendations are based on a small experiment that we have carried out with refactoring of UML class diagrams and state machines

From UML specification into FPGA implementation

In the paper a method of using the Unified Modeling Language for specification of digital systems, especially logic controllers, is presented. The proposed method is based mainly on the UML state machine diagrams and uses Hierarchical Concurrent Finite State Machines (HCFSMs) as a temporary model. The paper shows a way to transform the UML diagrams, expressed in XML language, to the form that is acceptable by reconfigurable FPGAs (Field Programmable Gate Arrays). The UML specification is used to generate an effective program in Hardware Description Languages (HDLs), especially Verilog

Generation of Software Test Data from the Design Specification Using Heuristic Techniques. Exploring the UML State Machine Diagrams and GA Based Heuristic Techniques in the Automated Generation of Software Test Data and Test Code.

Software testing is a tedious and very expensive undertaking. Automatic test data generation is, therefore, proposed in this research to help testers reduce their work as well as ascertain software quality. The concept of test driven development (TDD) has become increasingly popular during the past several years. According to TDD, test data should be prepared before the beginning of code implementation. Therefore, this research asserts that the test data should be generated from the software design documents which are normally created prior to software code implementation. Among such design documents, the UML state machine diagrams are selected as a platform for the proposed automated test data generation mechanism. Such diagrams are selected because they show behaviours of a single object in the system. The genetic algorithm (GA) based approach has been developed and applied in the process of searching for the right amount of quality test data. Finally, the generated test data have been used together with UML class diagrams for JUnit test code generation. The GA-based test data generation methods have been enhanced to take care of parallel path and loop problems of the UML state machines. In addition the proposed GA-based approach is also targeted to solve the diagrams with parameterised triggers. As a result, the proposed framework generates test data from the basic state machine diagram and the basic class diagram without any additional nonstandard information, while most other approaches require additional information or the generation of test data from other formal languages. The transition coverage values for the introduced approach here are also high; therefore, the generated test data can cover most of the behaviour of the system.EU Asia-Link project TH/Asia Link/004(91712) East-West and CAM

Does Code Generation Promote or Prevent Optimizations?

International audienceThis paper addresses the problem of code optimization for Real-Time and Embedded Systems (RTES). Such systems are designed using Model-Based Development (MBD)approach that consists of performing three major steps: building models, generating code from them and compiling the generated code. Actually, during the code generation, an important part of the modeling language semantics which could be useful for optimization is lost, thus, making impossible some optimizations achievement. This paper shows how adding a new level of optimization at the model level results in a more compact code. It also discusses the impact of the code generation on optimization: whether this step promotes or prevents optimizations. We conclude on a proposal of a new MBD approach containing only steps that advance optimization: modeling and compiling steps