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Tips and Tricks to Streamlining Software Development

Here are the top five tips for upgrading your software development process to a more streamlined practice. Software development is the core of any IT operation.

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Will AI render Software Developers Obsolete

Software development is the core of any IT operation. The importance of this line of work can be realized by the fact that 26.9 million developers are working all over the globe. This process is nothing more than translating the user’s needs into a software product at its core. However, we still see a lot of development projects being plagued by late finish dates, budget overruns, failure to meet business expectations, and in some cases, outright failure or project abandonment.

Here are the top five tips for upgrading your software development process to a more streamlined practice.

1. Have a Concrete Plan from Start

Like any other engineering project, software development relies vastly on planning. You need to set clear targets and make a strategy from the start to ensure that the process goes smoothly.

There is a massive communication gap between the client, developers, and the management. This leads to the developers not understanding the goals, and the result is compromised.

Suppose you want to ensure that your software development project is streamlined. In that case, the first and the most obvious step is to set concrete goals and divide the deliverables into attainable time-specific goals. This will avoid time and cost overruns and ensure that the client gets exactly what they are looking for.

Another thing that needs to be added here is that communication is the most important thing to make a software development project successful. Effective and timely communication between the development team and the client is necessary to get desired results.

2. Carefully Select the Target Platforms

There are a lot of desktop platforms and two major mobile platforms for developing apps. Windows and Mac are the most popular in the desktop world, while the duopoly of Android and iOS dominates the mobile world.

The thing to realize here is that you do not have to develop for every one of these platforms. If the client requires any specific platform(s), that’s what you need to focus on. Otherwise, select the target platform according to user demand and concentrate the development efforts on them.

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You can develop for all platforms, sure, but ask yourself: Are the users on a specific platform worth the effort needed to create for that? If yes, include that platform in your development process. If not, leave it alone and try to perfect the application for the forum you want to develop.

3. Build a Code Library

In modern software development, efficiency is the name of the game. A pro tip that can help you make your development process efficient is building a library of the codes you frequently use in your projects. This will save you from coding again and again from scratch, saving you a considerable amount of time.

There are a few things that you need to be mindful of while making a code library. First and foremost, the codes in the library need to be arranged in such a way that they can be accessed and used readily without having to waste time.

Secondly, you need to keep updating these codes regularly to ensure they do not have any vulnerability that can be exploited.

Thirdly, don’t rely on the code library too much. Only use the codes for repetitive processes that are common across apps. It is a good development practice to keep as much code unique for each app as possible.

4. Shift Left Testing

This is the most important tip for a streamlined software development lifecycle. Testing is not something to do after an app has been developed. It needs to be a part of the process from the very start.

Shift Left Testing is a practice where you start testing the software for all kinds of vulnerabilities as early on in the development process as possible. This makes sure that any threats, vulnerabilities, bugs, and other problems in the code are detected and rectified at earlier stages.

This also needs full and close collaboration of the development and testing teams if the project is big enough for dedicated teams to work on it. As mentioned earlier, timely and efficient communication between the development and security/testing teams is crucial for success.

Here’s an in-depth explanation of how shift left testing works and how it can help you streamline the development process.

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5. Automate Deployment

The last tip for efficient and streamlined development is to use tools to automate the application’s deployment process. There are a lot of tools available that can make it possible for you. Solutions like Dockers and Kubernetes can make it possible for you to automate deployment and scale the app without increasing the size of your team.

Another benefit of using these tools is that they can work across platforms, making it possible for you to deploy the app on more than one platform with little to no extra effort.

These tools work the best with containerized apps and can help you make logical units consisting of numerous containers to make deployment and scalability easier for you.

6. Conclusion

Software development is the heart and soul of IT. The most important thing to streamline the development process is to ensure that your efforts are converted into concrete results with the highest possible efficiency level.

You can simplify the software development process by making and following a plan from the first day, having effective communication, targeting specific platforms, building a library of commonly used codes, following the principles of shift-left testing, and leveraging the potential of deployment automation tools.

The most important thing of all these is making sure that testing is made a part of the software development lifecycle from the very start.

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Internet

How do collect and train data for speech projects?

Data collection is the process of gathering, analyzing, and, measuring accurate data from diverse systems to use for business process decision-making, speech projects, and research.

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How do collect and train data for speech projects

With technology evolution, we are moving towards machine learning systems that can understand what we say. In our daily lives, we all have encountered many virtual assistants like Alexa, Siri, and others. These virtual assistants often help us in tuning the lights of our homes, finding information on the internet, and even starting a video conference. But do you know how it does that?

To produce results, these virtual assistants use natural language processing to understand the user’s intent. Natural Language Processing technology enables virtual assistants to understand user intent and produce outcomes. Basically, these virtual assistants are applications of automatic speech recognition and are also known as speech recognition software. This software uses machine learning and NLP to analyze and convert human speech data into text.

But, attaining maximum efficiency of these software requires the collection of substantial speech and audio datasets. The purpose of collecting these audio datasets is to have enough sample recordings that can be fed into automatic speech recognition (ASR) software.

Furthermore, these datasets can be used against the speakers using unspecified speech recognition models. And to make ASR software work as intended, speech data collection and audio datasets must be conducted for all target demographics, locations, languages, dialects, and accents.

Artificial Intelligence can be as intelligent as the data given to it. Hence, collecting data for feeding the machine learning model is a must to maximize the effect of ASR. Let’s discuss steps in speech data collection for effective automatic speech recognition training.

1. Create a Demographic Matrix

For creating a demographic matrix, the enterprise must consider the following information like language, locations, ages, genders, and accents. Along with these, it is a must to note down a variety of information related to environments like busy streets, waiting rooms, offices, and homes. Enterprises can also consider the devices people are using like mobile phones, headsets, and a desktop.

2. Collect and transcribe speech data

To train the speech recognition model, gather speech samples from real humans and take the help of a human transcriptionist to take notes of long and short utterances by following your key demographic matrix. In this way, human is a vital and essential part of building proper audio datasets and labeled speech and further development of applications.

6 Reasons to Transcribe Audio to Text

3. Build a separate test data

Once the text subscription is completed, it’s time to pair the transcribed test with the corresponding audio data and segment them to include one statement in each. Later on, take the segmented pairs and extract a random 20% of the data to form a set for testing.

4. Train the language model

To maximize the effectiveness of the speech recognition model, you can train the language model by adding general additional text that was not additionally recorded. For example in canceling a subscription, you recorded one statement that ‘I want to cancel my subscription, but you can also add texts like “Can I cancel my subscription” or “I want to unsubscribe”. To make it more effective and catchy you can also add expressions and relevant jargon.

5. Measure and Iterate

The last and important step is to evaluate the output of automatic speech recognition software to benchmark its performance. In the next step take the trained model and measure how well it predicts the test set. In case of any gaps and errors, engage your machine learning model in the loop to yield the desired output. 

Conclusion

From travel, transportation, media, and entertainment, the use of speech recognition software is evident. We all have been using voice assistants like Alexa and Siri to complete some of our routine tasks. To effectively use this speech recognition software requires proper training in the audio datasets and the use of relevant data for the machine learning model.

Proper execution and the right use of data make sure the speech recognition software going to work efficiently and enterprises can scale them for further upgrades and development. As data and speech recognition go hand in hand, make sure you are using data with the right approach.

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