Krishnan Rangarajan

Deriving Combinatorial Test Design Model from UML Activity Diagram

Combinatorial test design model consists of parameters, values and the associated constraints. This model is the input for test generators. In this paper we present a rule based approach for deriving combinatorial test design model from UML activity diagrams resulting from requirement analysis. We have explored this approach on few sample cases. Our initial results suggest that this automated approach augmented with manual review and refinement step can make the test design task less effort intensive and more effective.

Extracting the Combinatorial Test Parameters and Values from UML Sequence Diagrams

In the current practice, the Combinatorial Test Design Model (CTDM) is designed by the test designers manually leveraging their experience in testing. Their involvement, perception, domain knowledge and testing proficiency are needed to analyze the requirements document and design the test model. Till date we know of no automated method that has eased the process of deriving the Combinatorial Test Design Model. Requirements document and analysis artifacts like UML activity diagrams and sequence diagrams hold information on parameters, values and constraints of the underlying CTDM. Our research focus is to develop a tool that assists test designers in coming up with the CTDM. This paper presents an approach to extract CTDM related information such as parameters and values from sequence diagrams. Our key contribution in this paper includes proposing a rule-based method for identifying the model elements from the sequence diagrams with the supporting rules and extraction algorithms. The rules have been applied onto individual sequence diagrams and results qualitatively discussed based on the general understanding of the requirements.

Building Combinatorial Test Input Model from Use Case Artefacts

Combinatorial Testing is a test design methodology that aims to detect the interaction failures existing in the software under test. The combinatorial input space model comprises of the parameters and the values it can take. Building this input space model is a domain knowledge and experience intensive task. The objective of the paper is to assist test designer in building this test model. A rule based semi-automatic approach is proposed to derive the input space model elements from Use case specifications and UML use case diagrams. A natural language processing based parser and an XMI based parser are implemented. The rules formulated are applied on synthetic case studies and the output model is evaluated using precision and recall metrics. The results are promising and this approach will be of good use to the test designer.

Computer vision based guidance in UAVs: software engineering challenges

In this paper we discuss the key functional and quality attribute requirements and the associated design challenges in engineering a computer vision guided UAV (Unmanned Aerial Vehicle) system. The non-functional requirements of the UAV system as a wholeare identified and mapped to the computer vision subsystem which aids in the navigation process. Expectations on availability, reliability, performance, security and evolution of the vision subsystemare discussed and the related software design challenges elaborated

Regression Test Suite Prioritization using Residual Test Coverage Algorithm and Statistical Techniques

Regression test suite study has been research topic for decades. In this paper we investigate the Regression test suite prioritization using residual test coverage algorithm for white box testing and introduce new statistical technique for black box testing. Our contribution in this paper is mainly problem solution to breaking the tie in residual coverage algorithm. Further we introduce new metric which can be used for prioritizing the regression test suite for black box testing. Towards the end part of the paper we get down to implementation details explaining how this can be done in the industrial or research project. The intended readers of this paper are developers and testers in the research field and practitioners of software engineering in the large scale industrial projects.

Integrated test environment for combinatorial testing

In this paper we propose an integrated test environment for combinatorial testing and discuss its realization at macro level. We describe the entities that make up the integrated test environment and their interaction, high-lighting the combinatorial test aspects. The proposed environment can be used for the automated as well as manual testing. Test tools supplied by research institutions and commercial tool vendors need to be integrated to realize the test environment proposed in this paper. The proposed system is theoretical in nature which can be embraced by large scale industrial and research projects to achieve an efficient integrated environment for combinatorial testing. This in turn can aid in the adoption of combinatorial testing to achieve the desired quality improvements in the products.

Analysis of Estimation Techniques for Feature Based Navigation of Unmanned Air Vehicles

This paper investigates different linear and non-linear state estimation techniques applicable in the scenario of unmanned air vehicle platform with an image sensor that attempts to navigate in a known urban terrain. Feature detection and matching steps are carried out using DTCWT (Dual tree Complex Wavelet Transform) descriptor. The state parameters of the vehicle are subsequently estimated using the measurements from the image sensor. Due to the non-linear nature of the problem, non-linear filters such as particle filters are often used. Various other methods have been proposed in the literature to the problem ranging from linear Kalman filter to non-linear probabilistic based techniques. This paper therefore attempts to study different state estimation methods and understand two main approaches in particular the Kalman and particle filter for the problem.

Re-architecture of Contiki and Cooja Regression Test Suites using Combinatorial Testing Approach

In this paper, we describe how combinatorial testing can be applied to re-architecture Contiki and Cooja regression test suites. Contiki is the popular and widely accepted internet of things operating system. Combinatorial testing was pioneered by National Institute of Standards and Technology. National Institute of Standards and Technology offers a set of tools to public. One such tool is Automated Combinatorial Testing for Software. We describe how Automated Combinatorial Testing for Software can be used to generate a complete test suite for Contiki and Cooja. Coverage of base test suite is gathered using CodeCover, a code coverage tool for Java. The low percentage of coverage in Cooja indicated the need for a redesign of test suite. Once the base regression test suite is modified using Combinatorial Testing approach, it can be the new base regression test suite.

Enhancing COCOA Framework for Tracking Multiple Objects in the Presence of Occlusion in Aerial Image Sequences

Multi object tracking in aerial image sequences is a topic of utmost importance in the field of computer vision for both military and civilian applications. In order to extract valid information about moving targets, it is required to detect and track these targets precisely in the input image sequences. Occlusion is one of the prominent problem areas that hinder efficient object tracking. Spatial reasoning literature fails to distinguish various analyses that are prominent to computer vision. However, recognizing valid occlusion states and mining their transition sequences help in analyzing the pose and motion of multiple interacting objects in the scene. In this paper, we propose an enhancement incorporating occlusion in the existing COCOA framework for tracking in aerial image sequence. The contribution of the paper is the novel idea of extracting occlusion cues as a pre-processing step to aid the tracker. We describe approaches to extract occlusion information in the scene and use it as a cue for efficient tracking.