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Introducing RECOVER: Streamlining Requirements Gathering

A tool designed to simplify requirements gathering from stakeholder conversations.

Gianmario Voria, Francesco Casillo, Carmine Gravino, Gemma Catolino, Fabio Palomba

― 6 min read


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Table of Contents

Requirements are a fancy word for what needs to be done for a software system to work. Imagine talking to a friend about what they want in a new video game – that’s what requirement gathering looks like. Now, picture a room full of people chatting away about what they need from a software system. It’s noisy, there are different ideas flying around, and it’s easy to get lost. This is where we introduce RECOVER, a new tool that aims to help make sense of all that chatter.

What is RECOVER?

RECOVER stands for "Requirements EliCitation frOm Conversations." Quite the mouthful, huh? But let’s keep it simple. It’s a tool designed to listen to conversations among stakeholders, figure out what they need, and turn those needs into clear requirements.

When people talk about what they want from a system, they often share useful insights, but manually sorting through all that can be a headache. That’s where RECOVER steps in to do the heavy lifting.

Why Do We Need It?

In today's fast-paced tech world, requirements gathering is critical. If you miss key details, the end software product might miss the mark entirely. Nobody wants a software system that can't even fulfill basic needs, right? Plus, humans can easily overlook things or twist the facts, leading to mistakes. So, automating this process is a big win.

The Challenge of Gathering Requirements

Think of it like this: when gathering requirements, there are two main problems. First, it’s time-consuming. You have to sit through meetings and discussions, trying to note everything – and trust me, that’s no walk in the park.

Second, humans are prone to errors. Ever misheard someone at a party? Imagine that happening in a meeting about what your software should do. Not exactly inspiring! So, we need a smart way to minimize these bumps in the road.

The RECOVER Solution

So, what does RECOVER actually do? It uses Natural Language Processing, which is basically a way for computers to understand human language. Think of it as teaching a baby to listen to everyone in the room and understand what they really mean. It tackles two major tasks: identifying requirements in conversations and generating those requirements in a clear format.

How Does RECOVER Work?

  1. Classification of Conversation Turns: RECOVER listens to snippets of conversations (like individual sentences) and decides if they contain useful Information about requirements. It acts like a filter, letting through only the good stuff.

  2. Processing Information: After identifying what might be a requirement, RECOVER cleans up the information. It discards details that don’t add value and organizes what’s left, often using question-and-answer formats. This makes later steps easier and more focused.

  3. Generating Requirements: Finally, it uses a large language model to create clear, concise requirements based on the processed information. So, after all that filtering and organizing, it can churn out a neat list of requirements for the software project.

What’s in it for Requirements Engineers?

The main takeaway is that RECOVER aims to shave off a big chunk of time and effort from the requirements-gathering process. This gives engineers more room to focus on the nitty-gritty details of what to build. Think of it like a trusty assistant making sure that you have everything you need without the annoying clutter.

Testing RECOVER

To see how well RECOVER works, a study was conducted. Experienced requirements engineers were brought in to evaluate its performance. They looked at whether RECOVER could find requirements in conversations and how well it generated them.

Accuracy Checks

When testing RECOVER, engineers first categorized conversation snippets as containing relevant requirements or not. The idea was to see how many times RECOVER got it right. Turns out, it scored pretty well! It could identify relevant information with a notable success rate.

The Quality of Outputs

After identifying snippets, RECOVER generated system requirements. Engineers assessed the quality of these requirements based on three key factors:

  1. Correctness: Did the generated requirements accurately reflect what was discussed?
  2. Completeness: Did it capture everything that needed to be mentioned?
  3. Actionability: Were the requirements clear enough to guide the next steps in development?

Overall, the engineers mostly agreed that RECOVER did a solid job. About 72% of them thought the requirements it generated were correct, while 64% felt they captured everything discussed.

A Day in the Life with RECOVER

Imagine being a requirements engineer. You start your day with a long list of meetings where stakeholders are discussing their needs. Instead of scribbling down notes like a mad person, you activate RECOVER.

As the conversations flow, RECOVER works in the background. At the end of the day, you receive a tidy list of requirements. You can spend your time reviewing this list rather than drowning in the chaos of raw conversation.

The Value of Automation

The beauty of automation is it takes the grunt work out of the process, allowing engineers to channel their expertise where it's needed most. Sure, technology isn’t perfect, and a few bumps may still occur. But in the grand scheme of things, reducing the load on engineers can lead to faster and more effective project outcomes.

Limitations of RECOVER

Not everything is sunshine and rainbows, though. While RECOVER is impressive, it still has room for improvement. Sometimes it may pick up irrelevant details, getting confused by the noise in conversations. And while its recall rate is high, it can suffer from precision issues, missing out on specific details that could make requirements better.

Looking Ahead

RECOVER isn’t just a one-and-done tool. The plan is to keep refining its capabilities. Future efforts might involve exploring even more complex algorithms, adapting to diverse styles of conversation, and improving how it contextualizes information.

Conclusion: A New Companion for Requirements Engineers

In summary, RECOVER shows great promise in transforming the tedious job of gathering requirements into a more manageable task. With its ability to filter out noise, process important information, and generate clear requirements, it’s like having a new sidekick ready to assist engineers in their daily quests.

So, the next time you think about the complicated world of requirements engineering, remember RECOVER. It’s here to help, making sure you don’t have to do all the heavy lifting alone. Now, go ahead and let RECOVER do the hard work while you kick back with a cup of coffee!

Original Source

Title: RECOVER: Toward the Automatic Requirements Generation from Stakeholders' Conversations

Abstract: Stakeholders' conversations in requirements elicitation meetings contain valuable information, but manually extracting system requirements from these discussions is a time-consuming and labor-intensive task, and there is a risk of errors and the introduction of biases. While current methods assist in summarizing conversations and classifying requirements based on their nature, there is a noticeable lack of approaches capable of both identifying requirements within these conversations and generating corresponding system requirements. These approaches would significantly reduce the burden on requirements engineers, reducing the time and effort required. They would also support the production of accurate and consistent requirements documentation. To address this gap, this paper introduces RECOVER (Requirements EliCitation frOm conVERsations), a novel requirements engineering approach that leverages NLP and foundation models to automatically extract system requirements from stakeholder interactions. The approach is evaluated using a mixed-method research design that combines statistical performance analysis with a user study involving requirements engineers. First, at the conversation turn level, the evaluation measures RECOVER's accuracy in identifying requirements-relevant dialogue and the quality of generated requirements in terms of correctness, completeness, and actionability. Second, at the entire conversation level, the evaluation assesses the overall usefulness and effectiveness of RECOVER in synthesizing comprehensive system requirements from full stakeholder discussions. The evaluation shows promising results regarding the performance of RECOVER, as the generated requirements exhibit satisfactory quality in their correctness, completeness, and actionability. Moreover, the results show the potential usefulness of automating the process of eliciting requirements from conversation.

Authors: Gianmario Voria, Francesco Casillo, Carmine Gravino, Gemma Catolino, Fabio Palomba

Last Update: Nov 29, 2024

Language: English

Source URL: https://arxiv.org/abs/2411.19552

Source PDF: https://arxiv.org/pdf/2411.19552

Licence: https://creativecommons.org/licenses/by/4.0/

Changes: This summary was created with assistance from AI and may have inaccuracies. For accurate information, please refer to the original source documents linked here.

Thank you to arxiv for use of its open access interoperability.

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