Research Flow

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Say, you are a student, and I’m your teacher. Your task is to conduct an experiment or a study and then write a research paper about it. You can do it on your own and then present me with the results in the end. Sometimes it may work, but most probably it won’t. I will have many comments, suggestions, and plain simple disagreements with your research questions, results, or conclusions. Just like in software engineering, the Waterfall approach is not an effective one. Instead, an incremental and iterative workflow may yield way better results: you take a small step forward, we discuss it, you rewrite, we agree, and you take the next step. The ultimate objective is to write a paper that will be published in a good journal or presented at a decent conference. Well, yes, a passing grade is also an objective.

Defend Me Against ChatGPT

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I do enjoy ChatGPT a lot. The blog post you’re reading now was written by me and then given to ChatGPT to fix its grammar and polish the writing style. Until recently, since 2014, when I wrote my first blog post, I used the service of a few proofreaders, who charged me $20-40 per hour to rewrite all of my 350+ texts. Now, I pay a few dollars a month to OpenAI. However, while the value of this generative AI is obvious, I also experience serious harm from ChatGPT, especially when reading papers written by my students with its help.

Review a Research Paper: Constructive Critique in Five Steps

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I’m helping organize the ICCQ conference this year for the fourth time, with the in-cooperation support of the IEEE Computer Society. Based on this short-term experience, I can assert that reviewing research papers is a skill that even some reputable and experienced academicians either don’t possess or are too lazy to apply. We often encounter sketchy, subjective, and disputable reviews that don’t assist authors but only frustrate and discourage them. In this short blog post, as an absolute amateur in the subject matter, I will try to summarize how to review an academic research paper (thus mostly helping other newbies).

Results and Discussion: Facts and Interpretation

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Almost every empirical research paper contains two essential sections: Results and Discussion. The former presents the facts collected through the research method, while the latter interprets them to answer the research questions. When interpreting the data, you must address the most obvious concerns that readers may have. For example, in the Results section, you might state: “85% of respondents refused to participate in our survey” (this is a fact). Then, in the Discussion section, you might say: “We believe that programmers are innately lazy and irresponsible” (this is an interpretation). You might also add, “Perhaps not all of them were lazy, but just busy.” While the Results section leaves no room for doubt, summarizing findings “as is,” the Discussion section engages in an open debate with an imaginative reader.

Be Indirect in Your Research Questionnaire to Gain More Honesty

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Let’s say you are conducting research to discover programmers’ opinions about their work environments: whether they appreciate their office spaces or not. Preparing a survey with a few questions is essential. Their responses will reveal their thoughts and feelings. After working with several student groups, I’ve noticed a common mistake in questionnaire design—they are too obvious with their questions, simply asking, “How do you feel about this?” There’s a more effective approach.

Avoid Soft Line Breaks Inside a Paragraph

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An email, a document, a research paper, a presentation, and even a JavaDoc code block consist of paragraphs, which are “self-contained units of discourse in writing dealing with a particular point or idea.” Visually, paragraphs are supposed to be separated by a vertical space that is a bit larger than a vertical spacing between lines. To achieve this, for example, in HTML, we wrap paragraphs in the <p> tag, while in LaTeX, we use \par or just an empty line between them. However, some people insert what are calledsoft line breaks” inside paragraphs—this is a bad practice that I suggest you stay away from.

The Method Section: A Recipe for Research

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Every empirical research paper must have a section titled “Method” (or “Methodology,” or “Study Design”). In this section, you describe what was done to obtain the data presented in the following “Results” section. You explain the recipe, which may be replicated later by another researcher, leading to the same (or very similar) results.

Related Work: A Critical Taxonomy of Prior Art

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In almost any research paper, it is mandatory to have a section titled “Related Work” (or “Related Works”), where you refer to the results previously obtained by other authors or yourself. By including this section in your paper, you are not only paying respect to those who laid the foundation for your results, but also motivating and guiding your readers. Most of them may not be aware of existing studies or the importance of the problem you are solving. You must put these guys into context.

Five Ingredients of Tech Career

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A friend of mine recently asked me what five things he should do in order to grow his technical career in a big company. He is not interested in being a big manager, or a CEO. Rather, he wants to be a software expert, an architect, an owner of a technology, and eventually a “Fellow.” I’m not sure I was qualified to give such advice, but I did anyway. This is what I told him. Maybe this will also work for you.

Bibcop: Style Checker for BibTeX

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Almost every document you may write in LaTeX format will have a list of references at the end. Most likely, you will use BibTeX or BibLaTeX to print this list of references in a nicely formatted way. It is also highly probable that your .bib file will contain many typographic, stylistic, and logical mistakes. I’m fairly certain that you won’t find the time to identify and correct them. As a result, the “References” section in your paper may appear sloppy. I suggest using the bibcop package, which identifies mistakes in the .bib file and auto-fixes some of them.

Robots vs. Programmers

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The release of ChatGPT 3.5 has changed everything for us programmers. Even though most of us (including me) don’t understand how it works, some of us use it more frequently than Stack Overflow, Google, and IDE built-in features. I believe this is just the beginning. Even though, only Microsoft knows what will happen next, let me try to make a humble prediction too. Below, I list what I believe robots (with Generative AI on board) will do in the future. The further into the future, the lower on the list. I tried not to repeat what GitHubNext is already saying.

Fast Tests Help Humans, Deep Tests Help Servers

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In order to reveal errors of higher complexity, automated tests are turned into integration tests that involve external resources in test scenarios, instead of their mocks. While this approach improves test coverage, it slows down the entire build pipeline. This compromises the very idea of automated tests, which are meant to be a safety net and help programmers edit code safely. Splitting the tests into “fast” and “deep,” and then allowing humans to run the former while servers run the latter, might be a good solution to the problem.

The Double-Blind Review Is What Makes Decisions Fair

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How does your team review ideas, project proposals, or paper drafts? Imagine I’m on your team and I need a budget allocated to a new project. I craft a proposal, elucidate the motivation, assess the risks, develop a plan, and then what? Do I create a PowerPoint presentation, present to my teammates for half an hour, answer their questions for another half hour, hear their honest feedback, after which they vote and a decision is made? If this is how things are organized within your team, you risk stifling creativity and motivation.

Is Two-Step Initialization a Solution or a Symptom?

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At times, it might appear practical to execute additional initialization steps for an object after its constructor has completed. However, I’m of the belief that such requirements signal underlying design flaws, such as object mutability, base class fragility, violation of layering, and unfocused abstraction. A constructor should be good enough for all scenarios. If it’s not, refactor the object.

The Hidden Dangers of Method Overloading

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Method overloading is a common feature in many programming languages that allows a class to have two or more methods with the same name but different parameters. According to Microsoft, method overloading is “one of the most important techniques for improving usability, productivity, and readability of reusable libraries.” I disagree. In my opinion, method overloading may lead to less readable code and more bugs, because maintaining two or more implementations under the same name gives rise to concealed semantics, which inevitably result in misunderstandings and functional defects.

A Disabled Test In Lieu of a Bug Report

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When you find a bug in an open-source library that you use, what do you do? You submit a GitHub issue (or whatever ticket tracking system they use). In the issue, you describe the problem in the best possible way, preferably providing a working code example that the author of the library can run locally to reproduce the bug. If you don’t provide them with an executable snippet of code, they will most likely ask you to do so, unless the bug is trivial. I suggest simplifying the workflow and giving them what they want right in a pull request: send them the bug in the form of a disabled unit test.

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