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Tag: machinelearning

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Computer Science

Artificial Intelligence-Based Software Engineering Metrics Defect Identification

Artificial Intelligence, Software Engineering

Nowadays we see the use of Artificial Intelligence (AI) in every field of study, even in image processing, data analytics, text processing, robotics, industries, software technologies, etc. Day by day increasing the use of AI helps users to perform automated tasks without involving the human physically present. The work can be completed automatically within the specified time as per the requirement. Even in the field of Software Engineering, the use of AI is growing day by day to perform automated tasks without causing errors. AI in software engineering provides automated non-error calculations, in the field of software engineering it provides the automated testing of software, automatic debugging of software, etc. to provide high quality (quality assurance), and efficiency of software applications within the budget.

Software Engineering is the method of developing, testing, and deploying computer software to solve problems and issues in the real world by utilizing software principles and best practices. It provides an organized and professional approach from developing the software to the deployment of the software. In the field of software engineering software metrics play a very important role. The software metrics are used to evaluate the reusability, quality, portability, functionality, and understandability. The AI-based software metrics are error-free and automated, they will used to identify or predict the defects in the software and efficient solutions in the real world scenario without involving humans.

Background

Many researchers describe different software metrics and provide efficient solutions for the software metrics. Some research also describes automated solutions using deep learning techniques to improve the software metrics. Without using Artificial intelligence, some chances degrade the quality of the final software products. It may also include a lack of functionality and require more human interactions due to which it increases the cost of the software. Some researchers describe deep learning-based techniques to solve software metrics problems and provide efficient results. However, the can be a lack in using the dataset and training of the data. Using the correct dataset may cause erroneous training of data and provide the wrong results. The wrong results may cause issues and degrade the quality of the software metrics. Therefore, this blog provides the proposed solution that aims to solve the problem of software metrics using Artificial Intelligence.

Basic Concept

According to the authors, there are several studies considered and provided many definitions. Therefore, before discussing the proposed approach let us discuss some basics about the software metrics as discussed.

As discussed above the software metrics are used to evaluate the reusability, quality, portability, functionality, and understandability of the software to achieve high-quality software. The software, metrics are of two types: the system-level metrics and component-level metrics as described in Figure 1.

Software Metrics Categories

Figure 1: Software Metrics Categories

  1. System Level Metrics: The system level metrics are further divided into three types that is Complexity, Metrics Suite, and System Complexity.
  2. Component level Metrics: The component level metrics are further divided into four types that are Customization, component complexity, reusability, and coupling and cohesion metrics.
  • Complexity Metrics: The complexity metrics are the type of system-level metrics. There are many definitions given by many authors. According to IEEE, complexity is defined as the quality where the component or any system design and implementation is complex to understand and authenticate.
  • Metric Suite: It provides the requirements and functionality of the software that is needed by the users. It ensures to provide users a high quality, satisfactory fault-free software products.
  • System Complexity Metric: It is defined as the component metric having a set of components in the system.
  • Customization: The customization metric identifies whether the component can be customized according to the user’s needs or not.
  • Component Complexity Metrics: The Component Complexity Metrics have component plain complexity (CPC), Component Static Complexity (CSC), and Component Dynamic Complexity (CDC).
  • Component Reusability Metric: This type of metric is the ratio of the sum of interface methods that provides common reuse component features used in a feature.
  • Coupling & Cohesion metrics: It is the degree or power with which software components are related to each other.

Proposed Approach

The proposed approach describes the AI-based method to identify as well as predict the defects in the software metrics. In this proposed approach, first, the real-time software metrics dataset is collected from multiple sources. The software metrics dataset may include the data regarding the identified software metrics with labels. The Figure 2 describes the architecture of the proposed approach and systematic details process of this approach.

Figure 2: Proposed Approach

  1. Obtaining data: This step is very essential and very important step of the approach. Correct data means high performance with minimum defects and results in high-quality software. Therefore, in this step, the labeled dataset of the software metrics will be collected. The dataset will include above 50K software modules and the number of defects predicted in the software module. The predicted defects can be binary values in the form of zero and one.
  2. Data Pre-Processing: After data, collection data pre-processing is an essential step of this proposed approach. After data collection, the data must be cleaned and normalized properly to make the analysis simpler and more efficient. In this step, the data is cleaned by removing empty rows, duplicate values, indexing of data, etc.
  3. Artificial Intelligence Model: After data, cleaning the Artificial Intelligence Model will be applied that analyzes the data such as determining the combinations, and automating the detection procedure without human interaction. AI model can be any algorithm applied such as Linear Regression, Logistic Regression, Naïve Bayes, Support Vector Machine, etc. These algorithms will analyze the data and determine the combinations. In this step, the data can also be converted into some kind of vectors also known as numbers based on the software metrics given in the dataset. The AI model is also known as the machine-learning model.
  4. Training and Testing: After the AI model is applied, the data will be split into train sets and test sets that are different from each other. 75% data is given for the train set and the remaining 25% of the data is given for the test set. Then the model is trained on the train set that will train the model based on the combinations and the test set will be used for our model that does not contain any labels, the machine will identify the faults automatically based on the training of the model.
  5. Results: After applying the AI or ML model on the test set the results are obtained on the test set that will determine how best the model works. The results will be determined in terms of accuracy, the area under the curve, f1-score, confusion matrix, etc. The expected accuracy will achieve 98% by applying logistic regression on the test set.

This blog is inspired and in contributed with Dr. Kiran Narang, Department of Computer Science Engineering, SRM University

REFERENCES

[1]. https://www.techtarget.com/whatis/definition/software-engineering
[2]. https://ieeexplore.ieee.org/document/8443016
[3]. https://en.wikipedia.org/wiki/Software_metric
[4]. https://www.sciencedirect.com/science/article/abs/pii/S0925231219316698
[5]. https://www.mdpi.com/2227-7390/10/17/3120
[6]. https://www.sciencedirect.com/science/article/pii/S0164121222002138
[7]. https://viso.ai/deep-learning/ml-ai-models/

Categories
Computer Science Electronics

Nextech AR Launches New SaaS Software Platform “Toggle3D” For CAD-3D Model Market

3D Model, Nextech, Intellect Partners
3D Model

Nextech AR Solutions Corp, a Metaverse Organization and leading supplier of augmented reality (“AR”) experience innovations and 3D model administrations is to report it has launched its Toggle3D, another artificial intelligence powered SaaS platform that empowers the creation, plan, design, and sending of 3D models at scale.

The Organization sees this major launch as a significant achievement while heading on its way to becoming the dominant 3D model platform and anticipates Toggle3D turning into another high-margin engine of development.

Toggle3D is an independent web application that empowers product designers, 3D artists, marketing professionals, and eCommerce website owners to make, customize and publish high-quality 3D models and experiences with no technical or 3D design information required.

The Organization accepts that Toggle3D is the first platform of its kind, and this break-through edge SaaS product is an expected huge advantage for the assembling and design industry, as it gives a feasible answer for converting large CAD (computer-aided design) documents into lightweight 3D models at reasonable prices and scale.

CAD (computer-aided design) is a component of product engineering. Modern engineers, working for product manufacturers, use CAD software like AutoCAD, and SolidWorks to plan large numbers of products in the modern world.

The Toggle3D platform utilized artificial intelligence so those raw CAD files can be converted into photo-realistic, fully textured 3D models at scale. Toggle3D innovation makes optimized 3D meshes that are suitable for 3D and AR applications.

The utilization of CAD documents is universal across assembling verticals including; aerospace, industrial machinery, automotive, civil and construction, pharmaceutical, healthcare, consumer goods, electronics, and others.

As per some reports, the CAD market, measured by the sum spent on the creation of CAD documents, is 11 billion dollars by 2023, so the Organization sees significant use cases for this.

Toggle3D utilizes Nextech AR’s patent-pending innovation artificial intelligence (enabling the transformation of CAD files into 3D models at scale) as well as the Organization’s ARitize Configurator product.

Creators can undoubtedly change their CAD files into 3D models, or carry their current 3D models into the platform. Inside the platform, creators will be able to direct 3 kinds of projects which are all templatized for a fantastic client experience: 3D product Configurators, Virtual Photography, and Item Demos being rolled out throughout the following couple of weeks.

To rapidly acquire early adopters, Toggle3D will be accessible both through a free trial and a pro-SaaS license. A free license will give clients barely enough functionality to evaluate the platform and experiment with the innovation. By upgrading to a pro plan, clients will be given full access to the whole platform’s features.

This incorporates limitless projects, more materials, bigger document uploads, more storage capacity, and other high-level editing tools. It is a self-serve platform, that contains a broad pre-built library of excellent quality 1000 PBR materials. Toggle3D makes things simple, with a friendly UI that works for the users and makes the whole 3D journey consistent.

Categories
Automotive Mechanical

Waymo is teaming up with Uber on autonomous trucking after a patent infringement standoff

Waymo is teaming up with Uber on autonomous trucking after a patent infringement standoff

Waymo and Uber, previous legal foes and harsh opponents in the autonomous vehicle space, are collaborating to accelerate the adoption of driverless trucks. Waymo is incorporating Uber Freight, the ride-hail organization’s truck business, into the innovation that powers its autonomous big rigs.

This “long-term strategic partnership” will enable fleet owners to more rapidly send trucks equipped with Waymo’s autonomous “driver” for on-demand delivery courses presented by Uber Freight, the organizations said.

The declaration addresses a union between two of the organizations’ significant side projects. Waymo separates its autonomous projects into two divisions: Waymo One, its consumer ride-hailing service, and Waymo Via, which is centered around goods delivery in both trucking and local delivery formats. Uber Freight, which was launched in 2017, connects drivers with shippers, much similar to the organization’s ride-hailing application that matches drivers with those searching for a ride.

Waymo depicts the collaboration as a “deep integration” of each organization’s products, including a mutually developed “product roadmap” to outline how autonomous trucks will get conveyed to Uber’s organization once they are commercially ready. Up to that point, Waymo says it will utilize Uber Freight with its test fleet to better comprehend how driverless trucks will receive and accept delivery orders.

Yet, the partnership goes past beta testing each other’s innovation. Waymo said it will save “billions of miles of its goods-only capacity for the Uber Freight network” in a capacity commitment intended to highlight the seriousness of this partnership.

In the not-so-distant past Waymo and Uber were in a grueling standoff over the eventual fate of autonomous vehicles. In February 2017, the Alphabet-owned organization sued Uber and its auxiliary, self-driving truck startup, Otto, over charges of trade secret theft and patent infringement. Waymo looked for $1.4 billion and a public apology from Uber, however, the ride-hail organization dismissed it as a non-starter.

The case went to trial for almost a year, however, finished quickly when the two sides reached an unexpected settlement agreement. Uber later conceded that it misappropriated a portion of Waymo’s tech and promised to permit it for future use. Anthony Levandowski, a previous Google engineer and the organizer behind Otto, was sentenced to 18 months in jail for taking Waymo’s trade secrets however was subsequently pardoned by former President Donald Trump.

There is no notice of past indiscretions in the declaration. Uber had been developing its self-driving truck as a feature of its bigger interest in autonomous innovation however later off-stacked it to Aurora, a startup established by the previous head of Waymo when it was only Google’s self-driving vehicle project. Expanding costs, in addition to the misfortune in Arizona when an Uber self-driving vehicle struck and killed a passerby, constrained Uber to take back its AV project.

Waymo has made a flurry of arrangements lately pointed toward developing its nascent trucking business. The Google spinoff has said it has no plans to possess or work its fleet of trucks and on second thought will work with truck manufacturers, carriers, and representatives to coordinate its innovation into the business of hauling freight.