29,075 research outputs found
A Survey on Software Testing Techniques using Genetic Algorithm
The overall aim of the software industry is to ensure delivery of high
quality software to the end user. To ensure high quality software, it is
required to test software. Testing ensures that software meets user
specifications and requirements. However, the field of software testing has a
number of underlying issues like effective generation of test cases,
prioritisation of test cases etc which need to be tackled. These issues demand
on effort, time and cost of the testing. Different techniques and methodologies
have been proposed for taking care of these issues. Use of evolutionary
algorithms for automatic test generation has been an area of interest for many
researchers. Genetic Algorithm (GA) is one such form of evolutionary
algorithms. In this research paper, we present a survey of GA approach for
addressing the various issues encountered during software testing.Comment: 13 Page
FORTEST: Formal methods and testing
Formal methods have traditionally been used for specification and development of software. However there are potential benefits for the testing stage as well. The panel session associated with this paper explores the usefulness
or otherwise of formal methods in various contexts for improving software testing. A number of different possibilities for the use of formal methods are explored and questions raised. The contributors are all members of the UK FORTEST Network on formal methods and testing. Although
the authors generally believe that formal methods
are useful in aiding the testing process, this paper is intended to provoke discussion. Dissenters are encouraged to put their views to the panel or individually to the authors
Evolutionary improvement of programs
Most applications of genetic programming (GP) involve the creation of an entirely new function, program or expression to solve a specific problem. In this paper, we propose a new approach that applies GP to improve existing software by optimizing its non-functional properties such as execution time, memory usage, or power consumption. In general, satisfying non-functional requirements is a difficult task and often achieved in part by optimizing compilers. However, modern compilers are in general not always able to produce semantically equivalent alternatives that optimize non-functional properties, even if such alternatives are known to exist: this is usually due to the limited local nature of such optimizations. In this paper, we discuss how best to combine and extend the existing evolutionary methods of GP, multiobjective optimization, and coevolution in order to improve existing software. Given as input the implementation of a function, we attempt to evolve a semantically equivalent version, in this case optimized to reduce execution time subject to a given probability distribution of inputs. We demonstrate that our framework is able to produce non-obvious optimizations that compilers are not yet able to generate on eight example functions. We employ a coevolved population of test cases to encourage the preservation of the function's semantics. We exploit the original program both through seeding of the population in order to focus the search, and as an oracle for testing purposes. As well as discussing the issues that arise when attempting to improve software, we employ rigorous experimental method to provide interesting and practical insights to suggest how to address these issues
A Systematic Review of the Application and Empirical Investigation of Search-Based Test Case Generation
Otsingupõhine tarkvara testimine kasutab metaheuristilisi algoritme, et automatiseerida testide genereerimist. Selle töö eesmärgiks on osaliselt taasluua 2010. aastal kirjutatud Ali et al. artikkel, et uurida, kuidas on aastatel 2008-2015 kasutatud metaheuristilisi algoritme testide loomiseks. See töö analüüsib, kuidas on antud artiklid koostatud ning kuidas neis on algoritmide maksumust ja efektiivsust hinnatud. Kogutud tulemusi võrreldakse Ali et al. tulemustega.Search based software testing uses metaheuristic algorithms to automate the generation of test cases. This thesis partially replicates a literature study published in 2010 by Ali et al. to determine how studies published in 2008-2015 use metaheuristic algorithms to automate the generation of test cases. The thesis analyses how these studies were conducted and how the cost-effectiveness is assessed in these papers. The trends detected in the new publications are compared to those presented in Ali et al
Innovative Techniques for Testing and Diagnosing SoCs
We rely upon the continued functioning of many electronic devices for our everyday welfare,
usually embedding integrated circuits that are becoming even cheaper and smaller
with improved features. Nowadays, microelectronics can integrate a working computer
with CPU, memories, and even GPUs on a single die, namely System-On-Chip (SoC).
SoCs are also employed on automotive safety-critical applications, but need to be tested
thoroughly to comply with reliability standards, in particular the ISO26262 functional
safety for road vehicles.
The goal of this PhD. thesis is to improve SoC reliability by proposing innovative
techniques for testing and diagnosing its internal modules: CPUs, memories, peripherals,
and GPUs. The proposed approaches in the sequence appearing in this thesis are described
as follows:
1. Embedded Memory Diagnosis: Memories are dense and complex circuits which
are susceptible to design and manufacturing errors. Hence, it is important to understand
the fault occurrence in the memory array. In practice, the logical and physical
array representation differs due to an optimized design which adds enhancements to
the device, namely scrambling. This part proposes an accurate memory diagnosis
by showing the efforts of a software tool able to analyze test results, unscramble
the memory array, map failing syndromes to cell locations, elaborate cumulative
analysis, and elaborate a final fault model hypothesis. Several SRAM memory failing
syndromes were analyzed as case studies gathered on an industrial automotive
32-bit SoC developed by STMicroelectronics. The tool displayed defects virtually,
and results were confirmed by real photos taken from a microscope.
2. Functional Test Pattern Generation: The key for a successful test is the pattern applied
to the device. They can be structural or functional; the former usually benefits
from embedded test modules targeting manufacturing errors and is only effective
before shipping the component to the client. The latter, on the other hand, can be
applied during mission minimally impacting on performance but is penalized due
to high generation time. However, functional test patterns may benefit for having
different goals in functional mission mode. Part III of this PhD thesis proposes
three different functional test pattern generation methods for CPU cores embedded
in SoCs, targeting different test purposes, described as follows:
a. Functional Stress Patterns: Are suitable for optimizing functional stress during
I
Operational-life Tests and Burn-in Screening for an optimal device reliability
characterization
b. Functional Power Hungry Patterns: Are suitable for determining functional
peak power for strictly limiting the power of structural patterns during manufacturing
tests, thus reducing premature device over-kill while delivering high test
coverage
c. Software-Based Self-Test Patterns: Combines the potentiality of structural patterns
with functional ones, allowing its execution periodically during mission.
In addition, an external hardware communicating with a devised SBST was proposed.
It helps increasing in 3% the fault coverage by testing critical Hardly
Functionally Testable Faults not covered by conventional SBST patterns.
An automatic functional test pattern generation exploiting an evolutionary algorithm
maximizing metrics related to stress, power, and fault coverage was employed
in the above-mentioned approaches to quickly generate the desired patterns. The
approaches were evaluated on two industrial cases developed by STMicroelectronics;
8051-based and a 32-bit Power Architecture SoCs. Results show that generation
time was reduced upto 75% in comparison to older methodologies while
increasing significantly the desired metrics.
3. Fault Injection in GPGPU: Fault injection mechanisms in semiconductor devices
are suitable for generating structural patterns, testing and activating mitigation techniques,
and validating robust hardware and software applications. GPGPUs are
known for fast parallel computation used in high performance computing and advanced
driver assistance where reliability is the key point. Moreover, GPGPU manufacturers
do not provide design description code due to content secrecy. Therefore,
commercial fault injectors using the GPGPU model is unfeasible, making radiation
tests the only resource available, but are costly. In the last part of this thesis, we
propose a software implemented fault injector able to inject bit-flip in memory elements
of a real GPGPU. It exploits a software debugger tool and combines the
C-CUDA grammar to wisely determine fault spots and apply bit-flip operations in
program variables. The goal is to validate robust parallel algorithms by studying
fault propagation or activating redundancy mechanisms they possibly embed. The
effectiveness of the tool was evaluated on two robust applications: redundant parallel
matrix multiplication and floating point Fast Fourier Transform
A testability transformation approach for state-based programs
Search based testing approaches are efficient in test data generation; however they are likely to perform poorly when applied to programs with state variables. The problem arises when the target function includes guards that reference some of the program state variables whose values depend on previous function calls. Thus, merely considering the target function to derive test data is not sufficient. This paper introduces a testability transformation approach based on the
analysis of control and data flow dependencies to
bypass the state variable problem. It achieves this by eliminating state variables from guards and/ or determining which functions to call in order to satisfy guards with state variables. A number of experiments demonstrate the value of the proposed approach
Search based software engineering: Trends, techniques and applications
© ACM, 2012. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version is available from the link below.In the past five years there has been a dramatic increase in work on Search-Based Software Engineering (SBSE), an approach to Software Engineering (SE) in which Search-Based Optimization (SBO) algorithms are used to address problems in SE. SBSE has been applied to problems throughout the SE lifecycle, from requirements and project planning to maintenance and reengineering. The approach is attractive because it offers a suite of adaptive automated and semiautomated solutions in situations typified by large complex problem spaces with multiple competing and conflicting objectives.
This article provides a review and classification of literature on SBSE. The work identifies research trends and relationships between the techniques applied and the applications to which they have been applied and highlights gaps in the literature and avenues for further research.EPSRC and E
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