The questions a paper should answer
A good research paper should answer the following questions:
- What, precisely, was your contribution?
- What question did you answer?
- Why should the reader care?
- What larger question this address?
- What is your new result?
- What new knowledge have you contributed that the reader can use elsewhere?
- What previous work (yours or someone else’s) do you build on? What do you provide a superior alternative to?
- How is your result different from and better than this prior work?
- What, precisely and in detail, is your new result?
- Why should the reader believe your result?
- What standard should be used to evaluate your claim?
- What concrete evidence shows that your result satisfies your claim?
What, precisely, was your contribution?
The paper suggests to first explain the problem and it’s importance before reporting what I did. To explain the problem, we first evaluate what our paper answers. The following table provides the question template for the types of research questions

The program committee looks for a clear statement of the specific problem you solved—the question about software development you answered—and an explanation of how the answer will help solve an important software engineering problem.
What is the new result?
Explain precisely what the contribution and its applications beyond my own project
The kinds of results are reported in this table

Commites look for interesting and novel results. Explain your result in such a way that someone else could use your ideas. Be sure to emphasize what is new in your idea. the application, the implementation, the analysis, or what?
Define critical terms precisely and use them consistently.
When presenting your research, ensure that your contributions are explicitly stated. Consider the following questions:
- What, precisely, do you claim to contribute?
- Do your results fully satisfy your claims?
- Are the definitions precise, and are terms used consistently?
Authors often encounter difficulties in specific situations. Below are common pitfalls and advice to avoid them:
- Scalability: If your result is intended for large systems, explain why you believe it scales effectively.
- Automation: If you claim your method is “automatic,” ensure it does not require human intervention. If manual assistance is needed for configuration, clarify this. If automation is limited to certain cases, specify them and indicate their frequency.
- Distributed Systems: If you claim your result is “distributed,” it should not rely on a single central controller or server. If it does, explicitly state which aspects are distributed and which are not.
- New Notation: If proposing a new notation for an established problem, justify why it is superior to existing notations.
- Experience Reports: If your paper is an experience report on using a previously reported tool or technique in a practical software project, clarify the key takeaway for readers. If the takeaway is increased confidence in the tool or technique, provide evidence supporting its applicability beyond your example.
- Basis of work and differentiating factor: Talk about the existing technology you are building and what have you done to improve it. What alternatives have been pursied and how is your work different.
Program committees are very interested in your interpretation of prior work in the area. They want to know how your work is related to the prior work, either by building on it or by providing an alternative.

- Novelty of Idea: Important to be clear about the claim, elucidate if required. The onus is on the author to explain. This table shows the reception to claims.
Use verbs that show results and achievement, not just effort and activity.

- Explaining the Results
- Clearly Define the Result
- Explicitly state what your result is and how it works, providing concrete examples.
- Explain its significance, novelty, and practical implications.
- Introducing a New Model
- Clearly describe its power and generality.
- Specify if it is based on empirical data, formal semantics, or mathematical principles.
- Define its level of formality—whether it is qualitative (providing design guidance) or mathematical (ensuring correctness).
- Assess whether the model scales to real-world problems within its domain.
- Defining a New Metric
- Provide a precise definition of the metric.
- Justify why it measures what it claims to measure and how it improves upon alternatives.
- Proposing a New Architectural Style, Design Pattern, or Design Element
- Compare it to existing alternatives, highlighting key differences and improvements.
- Explain the real problem it solves and whether it scales effectively.
- Synthesizing or Integrating Existing Components
- Justify why the integration itself is a contribution.
- Highlight the novelty, nonobvious insights, or generalizations derived from prior work.
- Clarify whether the synthesis improves individual components or leads to a new representation.
- Reporting Practical Application of Research
- Identify key lessons the reader can learn from your experience.
- Ensure conclusions are well-supported with comparative data or statistics.
- Distinguish between innovation and validation—if the research changed during application, clarify what was evaluated.
- Role of a Tool in the Contribution
- Specify whether the tool supports the main contribution, is itself a key innovation, or demonstrates a novel implementation.
- Clarify whether the idea can be applied independently of the tool.
- If the tool is a central part, what is the technical innovation embedded in the tool, for example using symlinks with a minio mount
- Role of a System Implementation
- Ensure the system functions as claimed and contributes to knowledge.
- Address the purpose of the implementation:
- Demonstrating an architecture/design strategy: Explain the rationale, tradeoffs, and applicability to other systems.
- Showcasing an implementation technique: Provide insights that help the reader apply the technique elsewhere.
- Validating a capability or performance improvement: Present concrete evidence supporting claims.
- Being the primary contribution: Prove it achieves something novel, especially if it was previously thought impossible.
Why should the reader believe your result?
Show evidence that your result is valid—that it actually helps to solve the problem you set out to solve.
It is import to select a form of validation, that is appropriate for the the research result and the method used to obtain the result. The following table in the paper provides the different type of validations you need to do

The most successful kinds of validation were based on analysis and real-world experience. Well-chosen examples were also successful. Persuasion was not persuasive, and narrative evaluation was only slightly more successful.
Writing the Abstract
Clearest abstracts had a common structure:
- Two or three sentences about the current state of the art, identifying a particular problem
- One or two sentences about what this paper contributes to improving the situation
- One or two sentences about the specific result of the paper and the main idea behind it
- A sentence about how the result is demonstrated or defended