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<a href="/20241210233721-brain_moc.html"> <img src="/assets/gr.png" alt="Site Logo" class="banner-logo" /> </a>
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<div id="updated">Updated: 2025-12-28 Sun 19:48</div>
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</div>
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<h1 class="title">ise<sub>week</sub><sub>4</sub></h1>
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<div class="title-metadata">
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<span class="metadata-item">
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<span class="metadata-label">planted:</span>
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<span class="metadata-value">2025-03-29</span>
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</span>
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<span class="metadata-item">
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<span class="metadata-label">last tended to:</span>
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<span class="metadata-value">2025-12-28</span>
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</span>
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</div>
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</div>
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<div id="outline-container-org5bbf239" class="outline-2">
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<h2 id="org5bbf239"><a href="#org5bbf239"><span class="done DONE">DONE</span> Different Coverage Metrics and Branch Concepts (pages 6-12)</a></h2>
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</div>
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<div id="outline-container-org2bf91f0" class="outline-2">
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<h2 id="org2bf91f0"><a href="#org2bf91f0"><span class="done DONE">DONE</span> Evolutionary Algorithm (pages 24-32)</a></h2>
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</div>
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||||
<div id="outline-container-orgb8f62f8" class="outline-2">
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||||
<h2 id="orgb8f62f8"><a href="#orgb8f62f8"><span class="done DONE">DONE</span> Test Case Generation (EvoSuite) (pages 44-55)</a></h2>
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</div>
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||||
<div id="outline-container-orge3d2f9a" class="outline-2">
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<h2 id="orge3d2f9a"><a href="#orge3d2f9a"><span class="done DONE">DONE</span> Multi/Many-objective Software Testing (Sapienz) (pages 80-97)</a></h2>
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<div class="outline-text-2" id="text-orge3d2f9a">
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<p>
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want to figure out how to translate the real world phenotype to a genotype
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</p>
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</div>
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</div>
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<div id="outline-container-orgd2bc261" class="outline-2">
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<h2 id="orgd2bc261"><a href="#orgd2bc261">4.2 Evolutionary Algorithms - Intelligent Software Engineering</a></h2>
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<div class="outline-text-2" id="text-orgd2bc261">
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</div>
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||||
<div id="outline-container-org1d9f9cb" class="outline-3">
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<h3 id="org1d9f9cb"><a href="#org1d9f9cb">1. Illustrative Optimization Problem</a></h3>
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<div class="outline-text-3" id="text-org1d9f9cb">
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<ul class="org-ul">
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<li>Problem: Maximize the objective function \( f(x) = x^2 \)</li>
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<li>Design variable: \( x \in \{-15, -14, ..., 0, 1, ..., 15\} \)</li>
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<li>Search space: All integers between -15 and 15 inclusive</li>
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<li>Objective function: \( f(x) = x^2 \), to be maximized</li>
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<li>Constraints: None</li>
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<li>This problem is simple and allows us to demonstrate the application of evolutionary algorithms without involving additional complexity from constraints.</li>
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</ul>
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</div>
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</div>
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||||
<div id="outline-container-org9c83d1b" class="outline-3">
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<h3 id="org9c83d1b"><a href="#org9c83d1b">2. Representation</a></h3>
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||||
<div class="outline-text-3" id="text-org9c83d1b">
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<p>
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||||
Evolutionary algorithms operate on representations of solutions called genotypes, which map to actual solutions (phenotypes). The choice of representation is crucial and problem-dependent.
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</p>
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||||
</div>
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||||
<div id="outline-container-org9d728e6" class="outline-4">
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||||
<h4 id="org9d728e6"><a href="#org9d728e6">Binary Representation</a></h4>
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||||
<div class="outline-text-4" id="text-org9d728e6">
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||||
<ul class="org-ul">
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||||
<li>The solution is represented as a fixed-length binary string.</li>
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||||
<li>For the example problem (maximizing \( f(x) = x^2 \)), we use a 5-bit binary representation:
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||||
<ul class="org-ul">
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||||
<li>The first bit indicates the sign of \( x \): 0 for positive, 1 for negative.</li>
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||||
<li>The remaining bits represent the magnitude in binary.</li>
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||||
</ul></li>
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||||
<li>The genotype space is \( \{0,1\}^L \), where L is the length of the binary string.</li>
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||||
</ul>
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||||
</div>
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||||
</div>
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||||
<div id="outline-container-org8e49233" class="outline-4">
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||||
<h4 id="org8e49233"><a href="#org8e49233">Other Common Representations</a></h4>
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||||
<div class="outline-text-4" id="text-org8e49233">
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||||
<ul class="org-ul">
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||||
<li><b><b>Binary</b></b>: Suitable for many simple problems.</li>
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||||
<li><b><b>Integer</b></b>: Useful for categorical or discrete variables (e.g., car brands such as Toyota, Volkswagen, etc.).</li>
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||||
<li><b><b>Floating Point</b></b>: Used for problems with continuous variables. For example, optimizing \( f(x_1, x_2) = x_1 + x_2 \), where \( x_1, x_2 \in [0,1] \).</li>
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||||
<li><b><b>Permutations</b></b>: Suitable for ordering problems like the Traveling Salesman Problem.</li>
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||||
<li><b><b>Matrices</b></b>: Employed in more complex problems such as staff allocation or scheduling.</li>
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||||
</ul>
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||||
</div>
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||||
</div>
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||||
</div>
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||||
<div id="outline-container-org95faefe" class="outline-3">
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||||
<h3 id="org95faefe"><a href="#org95faefe">3. Evolutionary Algorithm Steps</a></h3>
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||||
<div class="outline-text-3" id="text-org95faefe">
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||||
<p>
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||||
The typical steps in an evolutionary algorithm include:
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||||
</p>
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||||
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||||
<ol class="org-ol">
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||||
<li><b><b>Initialization</b></b>:
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||||
<ul class="org-ul">
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<li>Start with a randomly generated population of candidate solutions.</li>
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||||
<li>Ensure a diverse set of individuals to explore the search space effectively.</li>
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||||
<li>Optionally include known solutions or use heuristics to seed the initial population.</li>
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||||
</ul></li>
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||||
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||||
<li><b><b>Evaluation</b></b>:
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||||
<ul class="org-ul">
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||||
<li>Each individual is evaluated using a fitness function.</li>
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||||
<li>The fitness function quantifies how well an individual performs with respect to the problem objective.</li>
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||||
</ul></li>
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||||
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||||
<li><p>
|
||||
<b><b>Main Loop</b></b> (repeats until termination condition is met):
|
||||
a. <b><b>Selection</b></b>:
|
||||
</p>
|
||||
<ul class="org-ul">
|
||||
<li>Select parent individuals based on their fitness.</li>
|
||||
<li>Higher fitness individuals have a higher chance of being selected.</li>
|
||||
</ul>
|
||||
|
||||
<p>
|
||||
b. <b><b>Recombination (Crossover)</b></b>:
|
||||
</p>
|
||||
<ul class="org-ul">
|
||||
<li>Combine selected parents to produce new offspring.</li>
|
||||
<li>Occurs with probability \( P_c \) (crossover probability).</li>
|
||||
</ul>
|
||||
|
||||
<p>
|
||||
c. <b><b>Mutation</b></b>:
|
||||
</p>
|
||||
<ul class="org-ul">
|
||||
<li>Randomly alter offspring genes to maintain diversity.</li>
|
||||
<li>Occurs with probability \( P_m \) (mutation probability).</li>
|
||||
</ul>
|
||||
|
||||
<p>
|
||||
d. <b><b>Evaluation of Offspring</b></b>:
|
||||
</p>
|
||||
<ul class="org-ul">
|
||||
<li>Assess the fitness of each newly created individual.</li>
|
||||
</ul>
|
||||
|
||||
<p>
|
||||
e. <b><b>Survivor Selection</b></b>:
|
||||
</p>
|
||||
<ul class="org-ul">
|
||||
<li>Decide which individuals (from parents and offspring) will make up the next generation.</li>
|
||||
<li>Can use various strategies like elitism or generational replacement.</li>
|
||||
</ul></li>
|
||||
</ol>
|
||||
</div>
|
||||
</div>
|
||||
<div id="outline-container-org147f072" class="outline-3">
|
||||
<h3 id="org147f072"><a href="#org147f072">4. Fitness Function</a></h3>
|
||||
<div class="outline-text-3" id="text-org147f072">
|
||||
<ul class="org-ul">
|
||||
<li>The fitness function is derived from the problem’s objective or quality function.</li>
|
||||
<li>It assigns a single real-valued score to each individual (phenotype).</li>
|
||||
<li>The function reflects the degree to which a solution meets the desired criteria.</li>
|
||||
<li>Typically, the aim is to <b><b>maximize</b></b> fitness.</li>
|
||||
<li>If the problem is better posed as a minimization task, it can be transformed accordingly (e.g., minimizing \( f(x) \) is equivalent to maximizing \( -f(x) \)).</li>
|
||||
</ul>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
<div id="outline-container-org8c5c46d" class="outline-2">
|
||||
<h2 id="org8c5c46d"><a href="#org8c5c46d">4.3 Test Case Generation using EvoSuite - Intelligent Software Engineering</a></h2>
|
||||
<div class="outline-text-2" id="text-org8c5c46d">
|
||||
</div>
|
||||
<div id="outline-container-org5856127" class="outline-3">
|
||||
<h3 id="org5856127"><a href="#org5856127">1. Introduction to EvoSuite</a></h3>
|
||||
<div class="outline-text-3" id="text-org5856127">
|
||||
<ul class="org-ul">
|
||||
<li>EvoSuite is a tool developed by Fraser and Arcuri (2011) for automated test case generation.</li>
|
||||
<li>It generates whole test suites (not just individual test cases) for a given software system.</li>
|
||||
<li>The tool accepts a list of input classes to be tested and produces corresponding JUnit test case code.</li>
|
||||
<li>It leverages <b><b>genetic algorithms</b></b>, a form of evolutionary computation, to evolve effective test suites.</li>
|
||||
</ul>
|
||||
</div>
|
||||
</div>
|
||||
<div id="outline-container-org70324c2" class="outline-3">
|
||||
<h3 id="org70324c2"><a href="#org70324c2">2. Motivation and Limitations of Traditional Methods</a></h3>
|
||||
<div class="outline-text-3" id="text-org70324c2">
|
||||
<ul class="org-ul">
|
||||
<li>Conventional test generation tools typically focus on <b><b>single coverage goals</b></b> (e.g., a single program branch).</li>
|
||||
<li>Assumes:
|
||||
<ul class="org-ul">
|
||||
<li>All coverage goals are equally important.</li>
|
||||
<li>All goals are equally difficult to reach.</li>
|
||||
<li>Goals are independent of each other.</li>
|
||||
</ul></li>
|
||||
<li>These assumptions are problematic:
|
||||
<ul class="org-ul">
|
||||
<li>The sequence in which goals are selected can significantly affect the quality of the resulting test suite.</li>
|
||||
<li>Interdependencies among goals are often ignored.</li>
|
||||
</ul></li>
|
||||
</ul>
|
||||
</div>
|
||||
<div id="outline-container-org038fc64" class="outline-4">
|
||||
<h4 id="org038fc64"><a href="#org038fc64">Solution:</a></h4>
|
||||
<div class="outline-text-4" id="text-org038fc64">
|
||||
<ul class="org-ul">
|
||||
<li>Generate <b><b>whole test suites</b></b> rather than isolated test cases.</li>
|
||||
<li>Takes into account relationships between methods/classes.</li>
|
||||
</ul>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
<div id="outline-container-orgd97ff99" class="outline-3">
|
||||
<h3 id="orgd97ff99"><a href="#orgd97ff99">3. Architecture and Representation</a></h3>
|
||||
<div class="outline-text-3" id="text-orgd97ff99">
|
||||
</div>
|
||||
<div id="outline-container-orgfe7b811" class="outline-4">
|
||||
<h4 id="orgfe7b811"><a href="#orgfe7b811">Test Suite Representation</a></h4>
|
||||
<div class="outline-text-4" id="text-orgfe7b811">
|
||||
<ul class="org-ul">
|
||||
<li>A test suite \( T \) consists of multiple test cases.</li>
|
||||
<li>Each <b><b>test case</b></b> is a sequence of statements of varying types and lengths.</li>
|
||||
<li>The total length of a test suite is the sum of the lengths of its individual test cases.</li>
|
||||
</ul>
|
||||
</div>
|
||||
</div>
|
||||
<div id="outline-container-orgf6d0e5f" class="outline-4">
|
||||
<h4 id="orgf6d0e5f"><a href="#orgf6d0e5f">Statement Types in Test Cases</a></h4>
|
||||
<div class="outline-text-4" id="text-orgf6d0e5f">
|
||||
<ol class="org-ol">
|
||||
<li><b><b>Primitive statements</b></b>: Initialize basic types (e.g., `int var0 = 54`)</li>
|
||||
<li><b><b>Constructor statements</b></b>: Create new instances (e.g., `Stack var1 = new Stack()`)</li>
|
||||
<li><b><b>Field statements</b></b>: Access object members (e.g., `int var2 = var1.size`)</li>
|
||||
<li><b><b>Method statements</b></b>: Call methods (e.g., `int var3 = var1.pop()`)</li>
|
||||
</ol>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
<div id="outline-container-org9bf0ecd" class="outline-3">
|
||||
<h3 id="org9bf0ecd"><a href="#org9bf0ecd">4. Fitness Function</a></h3>
|
||||
<div class="outline-text-3" id="text-org9bf0ecd">
|
||||
<ul class="org-ul">
|
||||
<li>Guides the <b><b>selection of parents</b></b> in the genetic algorithm.</li>
|
||||
<li>Aims to <b><b>maximize code coverage</b></b>.</li>
|
||||
<li>If two test suites achieve the same coverage, the one with fewer statements is preferred (parsimony).</li>
|
||||
<li>Uses <b><b>branch coverage</b></b> as the primary metric.</li>
|
||||
<li>Employs the <b><b>branch distance heuristic</b></b>:
|
||||
<ul class="org-ul">
|
||||
<li>Measures how close an input is to flipping a predicate’s boolean outcome.</li>
|
||||
</ul></li>
|
||||
</ul>
|
||||
</div>
|
||||
</div>
|
||||
<div id="outline-container-orgd279d84" class="outline-3">
|
||||
<h3 id="orgd279d84"><a href="#orgd279d84">5. Bloat Control</a></h3>
|
||||
<div class="outline-text-3" id="text-orgd279d84">
|
||||
<ul class="org-ul">
|
||||
<li>A known issue in Genetic Algorithms is <b><b>bloat</b></b>, where test cases grow unnecessarily large.</li>
|
||||
<li>Can lead to memory exhaustion and inefficiency.</li>
|
||||
</ul>
|
||||
</div>
|
||||
<div id="outline-container-orga1d5e2f" class="outline-4">
|
||||
<h4 id="orga1d5e2f"><a href="#orga1d5e2f">Techniques Used:</a></h4>
|
||||
<div class="outline-text-4" id="text-orga1d5e2f">
|
||||
<ul class="org-ul">
|
||||
<li>Set limits:
|
||||
<ul class="org-ul">
|
||||
<li>Maximum number of test cases \( N \)</li>
|
||||
<li>Maximum length per test case \( L \)</li>
|
||||
</ul></li>
|
||||
<li>Discard offspring that do not provide improved coverage.</li>
|
||||
</ul>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
<div id="outline-container-org489def3" class="outline-3">
|
||||
<h3 id="org489def3"><a href="#org489def3">6. Search Operators</a></h3>
|
||||
<div class="outline-text-3" id="text-org489def3">
|
||||
</div>
|
||||
<div id="outline-container-orge95a449" class="outline-4">
|
||||
<h4 id="orge95a449"><a href="#orge95a449">Crossover Operator</a></h4>
|
||||
<div class="outline-text-4" id="text-orge95a449">
|
||||
<ul class="org-ul">
|
||||
<li>Combines two parent test suites (P1 and P2) to generate two offspring (O1 and O2).</li>
|
||||
<li>O1 = first \( a \cdot |P1| \) test cases from P1 + remaining from P2.</li>
|
||||
<li>O2 = similar combination from P2 and P1.</li>
|
||||
<li>Valid since test cases are independent.</li>
|
||||
<li>Helps reduce difference in length between resulting test suites.</li>
|
||||
</ul>
|
||||
</div>
|
||||
</div>
|
||||
<div id="outline-container-org406d40c" class="outline-4">
|
||||
<h4 id="org406d40c"><a href="#org406d40c">Mutation Operator</a></h4>
|
||||
<div class="outline-text-4" id="text-org406d40c">
|
||||
<ul class="org-ul">
|
||||
<li>Mutation is applied with a probability of \( 1/T \), where \( T \) is the number of test cases.</li>
|
||||
<li>New test cases may be added with a probability \( p \), up to a maximum count \( N \).</li>
|
||||
</ul>
|
||||
</div>
|
||||
</div>
|
||||
<div id="outline-container-org74087b9" class="outline-4">
|
||||
<h4 id="org74087b9"><a href="#org74087b9">Mutation Operations (applied with equal probability 1/3):</a></h4>
|
||||
<div class="outline-text-4" id="text-org74087b9">
|
||||
<ol class="org-ol">
|
||||
<li><b><b>Remove</b></b>:
|
||||
<ul class="org-ul">
|
||||
<li>Each statement \( s_i \) is deleted with probability \( 1/n \), where \( n \) is the number of statements.</li>
|
||||
<li>If needed, replace deleted statements to keep test case valid.</li>
|
||||
</ul></li>
|
||||
|
||||
<li><b><b>Change</b></b>:
|
||||
<ul class="org-ul">
|
||||
<li>Each statement \( s_i \) may be altered.</li>
|
||||
<li>For primitives: change numeric value randomly within ±Δ.</li>
|
||||
<li>For others: change to a method/field/constructor of the same type.</li>
|
||||
</ul></li>
|
||||
|
||||
<li><b><b>Insert</b></b>:
|
||||
<ul class="org-ul">
|
||||
<li>A new statement is inserted at a random position in the test case.</li>
|
||||
</ul></li>
|
||||
</ol>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
<div id="outline-container-org38987d1" class="outline-3">
|
||||
<h3 id="org38987d1"><a href="#org38987d1">7. Results and Evaluation</a></h3>
|
||||
<div class="outline-text-3" id="text-org38987d1">
|
||||
<ul class="org-ul">
|
||||
<li>Key takeaway: <b><b>EvoSuite outperforms traditional single-goal test generation tools</b></b>.</li>
|
||||
<li>Reported improvement: Up to <b><b>18x better branch coverage</b></b> than single-branch strategies.</li>
|
||||
</ul>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
<div id="outline-container-org4d8ac43" class="outline-2">
|
||||
<h2 id="org4d8ac43"><a href="#org4d8ac43">4.4 Multi/Many-objective Software Testing with Sapienz - Intelligent Software Engineering</a></h2>
|
||||
<div class="outline-text-2" id="text-org4d8ac43">
|
||||
</div>
|
||||
<div id="outline-container-org2ed8809" class="outline-3">
|
||||
<h3 id="org2ed8809"><a href="#org2ed8809">1. Introduction to Sapienz</a></h3>
|
||||
<div class="outline-text-3" id="text-org2ed8809">
|
||||
<ul class="org-ul">
|
||||
<li>Sapienz is an automated software testing tool developed by Mao et al. (2016).</li>
|
||||
<li>It uses evolutionary algorithms to generate test cases for Android apps.</li>
|
||||
<li>Notable Achievements:
|
||||
<ul class="org-ul">
|
||||
<li>Tested the top 1000 most popular Google Play apps.</li>
|
||||
<li>Discovered 558 unique and previously unknown app crashes.</li>
|
||||
<li>Led to a commercial spinout company named <b><b>MaJiCkE</b></b>.</li>
|
||||
<li>Acquired by <b><b>Facebook/Meta</b></b>.</li>
|
||||
</ul></li>
|
||||
|
||||
<li>Sapienz customizes the <b><b>NSGA-II</b></b> algorithm (a multi-objective genetic algorithm) for test case generation.</li>
|
||||
</ul>
|
||||
|
||||
<p>
|
||||
Reference: Mao, Ke, Mark Harman, and Yue Jia. <b>“Sapienz: Multi-objective automated testing for android applications.”</b> ISSTA 2016.
|
||||
</p>
|
||||
</div>
|
||||
</div>
|
||||
<div id="outline-container-org9fde7af" class="outline-3">
|
||||
<h3 id="org9fde7af"><a href="#org9fde7af">2. NSGA-II: An Overview</a></h3>
|
||||
<div class="outline-text-3" id="text-org9fde7af">
|
||||
<ul class="org-ul">
|
||||
<li>NSGA-II is a Genetic Algorithm (GA) adapted for <b><b>multi-objective optimization</b></b>.</li>
|
||||
<li>Key Differences from standard GA:
|
||||
<ul class="org-ul">
|
||||
<li>Uses <b><b>Pareto dominance</b></b> for survival selection.</li>
|
||||
<li>A solution <b><b>a dominates</b></b> solution <b><b>b</b></b> if:
|
||||
<ul class="org-ul">
|
||||
<li>\( a_i \leq b_i \) for all objectives, and</li>
|
||||
<li>\( \exists j \) such that \( a_j < b_j \)</li>
|
||||
</ul></li>
|
||||
<li>A <b><b>Pareto optimal</b></b> solution is one that is not dominated by any other in the population.</li>
|
||||
</ul></li>
|
||||
|
||||
<li>NSGA-II also uses:
|
||||
<ul class="org-ul">
|
||||
<li><b><b>Non-dominated sorting</b></b>: Separates population into Pareto fronts.</li>
|
||||
<li><b><b>Crowding distance</b></b>: Prefers diverse solutions within the same front.</li>
|
||||
</ul></li>
|
||||
</ul>
|
||||
</div>
|
||||
</div>
|
||||
<div id="outline-container-orge5fcd37" class="outline-3">
|
||||
<h3 id="orge5fcd37"><a href="#orge5fcd37">3. Representation</a></h3>
|
||||
<div class="outline-text-3" id="text-orge5fcd37">
|
||||
<ul class="org-ul">
|
||||
<li>Specific representation details were not included in the slides but are tailored to represent Android GUI interaction sequences.</li>
|
||||
</ul>
|
||||
</div>
|
||||
</div>
|
||||
<div id="outline-container-org74c5378" class="outline-3">
|
||||
<h3 id="org74c5378"><a href="#org74c5378">4. Objective Functions in Sapienz</a></h3>
|
||||
<div class="outline-text-3" id="text-org74c5378">
|
||||
<p>
|
||||
Sapienz optimizes multiple objectives simultaneously:
|
||||
</p>
|
||||
</div>
|
||||
<div id="outline-container-org3ca1c7d" class="outline-4">
|
||||
<h4 id="org3ca1c7d"><a href="#org3ca1c7d">a. Code Coverage</a></h4>
|
||||
<div class="outline-text-4" id="text-org3ca1c7d">
|
||||
<ul class="org-ul">
|
||||
<li>Types of coverage used:
|
||||
<ul class="org-ul">
|
||||
<li><b><b>Statement Coverage</b></b>: Measures how many individual code statements are executed.</li>
|
||||
<li><b><b>Method Coverage</b></b>: Measures the number of methods invoked.</li>
|
||||
<li><b><b>Android Activity Coverage</b></b>: Tracks which screens (activities) of the app are accessed.
|
||||
<ul class="org-ul">
|
||||
<li>Example: A dialer app may include separate activities for contacts, keypad, call history, etc.</li>
|
||||
</ul></li>
|
||||
</ul></li>
|
||||
</ul>
|
||||
</div>
|
||||
</div>
|
||||
<div id="outline-container-org7ec352d" class="outline-4">
|
||||
<h4 id="org7ec352d"><a href="#org7ec352d">b. Test Case Length</a></h4>
|
||||
<div class="outline-text-4" id="text-org7ec352d">
|
||||
<ul class="org-ul">
|
||||
<li>Shorter test cases are generally preferred to improve efficiency and reduce overhead.</li>
|
||||
<li>Multiple slides (86–89) emphasize the importance of minimizing test length.</li>
|
||||
</ul>
|
||||
</div>
|
||||
</div>
|
||||
<div id="outline-container-orga0794d3" class="outline-4">
|
||||
<h4 id="orga0794d3"><a href="#orga0794d3">c. Crash Discovery</a></h4>
|
||||
<div class="outline-text-4" id="text-orga0794d3">
|
||||
<ul class="org-ul">
|
||||
<li>The number of test cases that lead to app crashes is also a key metric.</li>
|
||||
<li>Objective: Maximize the number of crash-inducing test cases.</li>
|
||||
</ul>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
<div id="outline-container-org4f9c987" class="outline-3">
|
||||
<h3 id="org4f9c987"><a href="#org4f9c987">5. Search Operators</a></h3>
|
||||
<div class="outline-text-3" id="text-org4f9c987">
|
||||
</div>
|
||||
<div id="outline-container-org8b46a41" class="outline-4">
|
||||
<h4 id="org8b46a41"><a href="#org8b46a41">a. Crossover</a></h4>
|
||||
<div class="outline-text-4" id="text-org8b46a41">
|
||||
<ul class="org-ul">
|
||||
<li>Combines parts of two parent test sequences to form new offspring.</li>
|
||||
<li>Details of the crossover structure are tool-specific but follow the typical GA-style recombination.</li>
|
||||
</ul>
|
||||
</div>
|
||||
</div>
|
||||
<div id="outline-container-orge31bd90" class="outline-4">
|
||||
<h4 id="orge31bd90"><a href="#orge31bd90">b. Mutation</a></h4>
|
||||
<div class="outline-text-4" id="text-orge31bd90">
|
||||
<ul class="org-ul">
|
||||
<li>Mutations are applied to test cases to explore new behaviors.</li>
|
||||
<li>Types of Mutation:
|
||||
|
||||
<ol class="org-ol">
|
||||
<li><b><b>High-Level Mutation</b></b>:
|
||||
<ul class="org-ul">
|
||||
<li>Alters the structure or intent of test sequences.</li>
|
||||
</ul></li>
|
||||
|
||||
<li><b><b>Low-Level Mutation (Same Size)</b></b>:
|
||||
<ul class="org-ul">
|
||||
<li>Changes test actions without altering the sequence length.</li>
|
||||
</ul></li>
|
||||
|
||||
<li><b><b>Low-Level Mutation (Different Size)</b></b>:
|
||||
<ul class="org-ul">
|
||||
<li>Adds or removes actions to vary the length of test cases.</li>
|
||||
</ul></li>
|
||||
|
||||
<li><b><b>Low-Level Mutation (Shuffling)</b></b>:
|
||||
<ul class="org-ul">
|
||||
<li>Reorders existing actions in the test case.</li>
|
||||
</ul></li>
|
||||
</ol></li>
|
||||
</ul>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
<div id="outline-container-orgd727132" class="outline-3">
|
||||
<h3 id="orgd727132"><a href="#orgd727132">6. Results and Observations</a></h3>
|
||||
<div class="outline-text-3" id="text-orgd727132">
|
||||
<ul class="org-ul">
|
||||
<li>Sapienz significantly <b><b>outperforms other automated testing tools</b></b> in terms of:
|
||||
<ul class="org-ul">
|
||||
<li>Number of crashes detected.</li>
|
||||
<li>Coverage achieved.</li>
|
||||
<li>Efficiency in test generation.</li>
|
||||
</ul></li>
|
||||
</ul>
|
||||
|
||||
|
||||
|
||||
<p class="backlinks-section" id="backlinks">
|
||||
Backlinks
|
||||
</p>
|
||||
<ul class="org-ul backlinks-list">
|
||||
<li><a href="20250329114733-ise.html#ID-c69e4c4d-2fb4-4cf1-a835-a235cf6db8e9">ise</a></li>
|
||||
</ul>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</div></article></div></div>
|
||||
<div id="postamble" class="status">
|
||||
<footer>
|
||||
<div class="copyright-container">
|
||||
<div class="copyright">
|
||||
Copyright © 2022-2025 Zaine Qayyum. All rights reserved unless otherwise noted.</div></div>
|
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<div class="generated">
|
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Created with <a href="https://www.gnu.org/software/emacs/">Emacs</a> 30.2 (<a href="https://orgmode.org">Org</a> mode 9.7.11) on <a href="https://www.archlinux.org/">Arch</a> <a href="https://www.gnu.org">GNU</a>/<a href="https://www.kernel.org/">Linux</a>
|
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</footer>
|
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</div>
|
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</body>
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|
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Reference in New Issue
Block a user