360 lines
14 KiB
HTML
Executable File
360 lines
14 KiB
HTML
Executable File
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<div class="banner-header">
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<div class="banner-left">
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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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<div class="banner-search">
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<input id="search-box"
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autocomplete="off" />
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<div id="search-results"></div>
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<button id="close-all">Close All</button>
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</div>
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<div id="stack-root">
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<div class="stack-track">
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<article class="stack-pane pane-root" data-url="/20250329114848-ise_week_1.html">
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<button class="pane-fullscreen" aria-label="Fullscreen">F</button>
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<button class="pane-edit" aria-label="Edit pane">E</button>
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<button class="pane-close" aria-label="Close pane">×</button>
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</div>
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<h1 class="title">ise<sub>week</sub><sub>1</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-orgceadddf" class="outline-2">
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<h2 id="orgceadddf"><a href="#orgceadddf">Bug report classification</a></h2>
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<div class="outline-text-2" id="text-orgceadddf">
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<p>
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Perform bug report classification with AI following the steps
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below:
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</p>
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<ul class="org-ul">
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<li>Text filtering</li>
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<li>Stemming</li>
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<li>Indexing</li>
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<li>Train machine learning models, i.e., Decision Tree, Naive Bayes, and Logistic
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Regression</li>
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<li>Prediction (after data pre-processing)</li>
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</ul>
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</div>
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<div id="outline-container-org127eadf" class="outline-3">
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<h3 id="org127eadf"><a href="#org127eadf">Text filtering:</a></h3>
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<div class="outline-text-3" id="text-org127eadf">
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<ul class="org-ul">
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<li>Punctuation removal</li>
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<li>Specific transformations occur:
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the myUser has no meaning, its just a word. You need to split it into two meaningful words.</li>
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<li>Stop words should not be removed, as it changes the whole meaning of a bug report.</li>
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<li>Unlike other NLP tasks, removing stop words may distort the meaning of bug reports.</li>
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</ul>
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</div>
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</div>
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<div id="outline-container-org3a0936f" class="outline-3">
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<h3 id="org3a0936f"><a href="#org3a0936f">Stemming:</a></h3>
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<div class="outline-text-3" id="text-org3a0936f">
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<ul class="org-ul">
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<li>Stemming reduces different forms of a word to its common base by sequential application of stemming rules. For example: light caresses colours becomes Light caress colour</li>
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</ul>
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</div>
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</div>
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<div id="outline-container-org17a65cc" class="outline-3">
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<h3 id="org17a65cc"><a href="#org17a65cc">Indexing:</a></h3>
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<div class="outline-text-3" id="text-org17a65cc">
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<ul class="org-ul">
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<li>Indexing: we need to convert the text into numeric representation, there are two common ways of doing this: TF and IDF</li>
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<li>TF: total words / specific word . for example: “bug” appears 50 times in a 150 terms.</li>
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<li>IDF: We penalise certain words based on their frequency.</li>
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<li>bug report classifications here often does not use IDF but TF, because some frequent terms should not be penalized</li>
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<li>For each report, we will have a vector of indexing for words in the reports according to a dictionary</li>
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<li>Example: A bug report = {“this”, “bugs”, “failure”, “interesting”, …}
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Vector = <0.123, 0.34, 0.1, ….></li>
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</ul>
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</div>
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</div>
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<div id="outline-container-org67b71c1" class="outline-3">
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<h3 id="org67b71c1"><a href="#org67b71c1">DT</a></h3>
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<div class="outline-text-3" id="text-org67b71c1">
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<ul class="org-ul">
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<li>What is decision tree: a tree structure that split the data depending on different values.</li>
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<li>Each node is a test on the attribute.</li>
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<li>Each branch represents the outcome.</li>
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</ul>
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</div>
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</div>
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<div id="outline-container-orgddf667c" class="outline-3">
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<h3 id="orgddf667c"><a href="#orgddf667c">NB</a></h3>
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<div class="outline-text-3" id="text-orgddf667c">
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<ul class="org-ul">
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<li>What is Naive Bayes? Classifier based on the conditional probability given by the Bayes theorem.</li>
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<li>Calculating the probability of each class, and classify the given features into the one with higher probability.</li>
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</ul>
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||
</div>
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||
</div>
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<div id="outline-container-org77c7bef" class="outline-3">
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<h3 id="org77c7bef"><a href="#org77c7bef">LR</a></h3>
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<div class="outline-text-3" id="text-org77c7bef">
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<ul class="org-ul">
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<li>What is logistic regression: a linear model for classification</li>
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<li>Fitting the logistic growth and the sigmoid midpoint</li>
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<li>Output a probability, but can use a cutoff point to decide class.</li>
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</ul>
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||
</div>
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||
</div>
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||
<div id="outline-container-org7f2e242" class="outline-3">
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<h3 id="org7f2e242"><a href="#org7f2e242">Results:</a></h3>
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<div class="outline-text-3" id="text-org7f2e242">
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<p>
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Choose the top 20/50 words when theyre used as a feature
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</p>
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||
<p class="backlinks-section" id="backlinks">
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||
Backlinks
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||
</p>
|
||
<ul class="org-ul backlinks-list">
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||
<li><a href="20250329114733-ise.html#ID-c69e4c4d-2fb4-4cf1-a835-a235cf6db8e9">ise</a></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>
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||
</div></article></div></div>
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<div id="postamble" class="status">
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||
<footer>
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||
<div class="copyright-container">
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||
<div class="copyright">
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||
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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