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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

Inside LPDDR5: Driving Forces of 5G and AI Revolution

Inside LPDDR5: Driving Forces of 5G and AI Revolution

Understanding LPDDR5: Powering the 5G and AI Revolution:

In the ever-evolving landscape of innovation, the combination of 5G and artificial intelligence (AI) has emerged as a transformative force, reshaping enterprises and empowering developments that were previously unimaginable. Vital to this combination is the role of LPDDR5 (Low Power Double Data Rate 5) memory, a state-of-the-art memory innovation that assumes an essential part in supporting the high-performance demands of 5G and artificial intelligence applications. This blog entry dives into the meaning of LPDDR5 in these spaces, investigates its future patterns, and analyzes the most recent improvements in its intellectual property (IP).

LPDDR5 Overview

LPDDR5 is the fifth generation of low-power, high-performance memory planned essentially for smartphones. It is a development of its ancestor, LPDDR4x, with critical enhancements as far as information rate, power effectiveness, and generally execution. LPDDR5 offers quicker information move rates, lower power utilization, and larger memory capacities compared to its predecessors, settling on it an ideal decision for applications requesting high data transfer capacity and low latency.

Role in 5G

The rollout of 5G networks has introduced another time of availability, empowering lightning-quick information move rates and super low inactivity. To completely tackle the capability of 5G, memory devices should be equipped with memory advances fit for taking care of the expanded data loads and rapid communication among memory devices and edge servers. LPDDR5, with its upgraded information rates and further developed energy proficiency, addresses these requests by giving the important memory data transfer capacity and responsiveness for 5G-empowered gadgets.

Enabling AI Applications

Artificial intelligence applications, including AI and neural networks, require enormous measures of data processing and storage capabilities. LPDDR5’s high information move rates and bigger memory limits add to accelerating AI tasks by giving the fundamental memory resources to putting away and controlling information during preparation and inference processes. This is critical for AI-driven functionalities-driven functionalities in gadgets, for example, smartphones, smart cameras, and IoT gadgets.

Future Trends in LPDDR5 Technology

Data Rate Advancements

The journey for higher data rates proceeds, as innovation organizations endeavor to push the limits of memory execution. LPDDR5 is supposed to see further iterations that proposition considerably quicker information move rates, empowering consistent 5G network and improved AI performance.

Energy Efficiency

While LPDDR5 as of now offers amazing energy, effectiveness contrasted with its predecessors, progressing research and development efforts aim to diminish power utilization considerably further. This is especially significant for broadening the battery duration of gadgets, particularly with regards to power-hungry 5G and AI workloads.

Integration with On-Device AI

As AI capabilities are coordinated straightforwardly into devices, LPDDR5 will assume a critical part in supporting on-gadget artificial intelligence errands. This includes not just giving the memory resources to AI operations but also improving memory access examples to upgrade general artificial intelligence execution.

LPDDR5 IP Developments and Legal Considerations  

WCK Clocking in LPDDR5

LPDDR5 uses a DDR data interface. The data interface uses two differential forwarded clocks (WCK_t/WCK_c) that are source synchronous to the DQs. DDR means that the data is registered at every rising edge of WCK_t and rising edge of WCK_c. WCK_t and WCK_c operate at twice or quadruple the frequency of the command/address clock (CK_t/CK_c).

Low Power Double Data Rate (LPDDR) 5/5X
https://www.jedec.org/sites/default/files/docs/JESD209-5C.pdf

IP Landscape of LPDDR5

The intellectual property landscape for LPDDR5 innovation is dynamic and advancing. Organizations in the semiconductor industry are continuously creating and licensing developments connected with LPDDR5 memory configuration, fabricating processes, and related advancements. Licensing agreements and cross-licensing arrangements assume a vital part in permitting organizations to get to and use these IP resources.

Patent Challenges and Litigations  

With the rising competitive nature of the innovation business, patent disputes and litigations can emerge. Organizations should be cautious in surveying the potential infringement risks related to LPDDR5-related technologies and ought to participate in due diligence before creating items to stay away from legal complications.

Licensing Strategies  

Licensing LPDDR5-related IP is a typical methodology for organizations to get to the innovation without wasting time. Licensing arrangements frame the terms under which an organization can utilize licensed innovations, and they might include royalty payments or other monetary considerations. Developing a sound licensing procedure is fundamental to guarantee that organizations can use LPDDR5 innovation while regarding IP rights. Intel Corp. holds a maximum number of patents followed by Samsung and Micron.

IP Trends (Company vs Number of Patents)
https://www.lens.org/lens/search/patent/list?q=Application%20of%20LPDDR5%20memory&n=10 )
Patent legal status over time
Patent Publication status over time
https://www.lens.org/lens/search/patent/list?q=Application%20of%20LPDDR5%20memory&n=10

Conclusion

The integration of 5G and AI is revolutionizing businesses and changing the manner in which we connect with technology. LPDDR5 memory technology remains as a basic empowering influence of this change, giving the high-performance memory capabilities expected to help the requests of 5G network and AI applications. As LPDDR5 innovation keeps on developing, with headways in information rates and energy productivity, it will be interesting to observe how it shapes the future of mobile devices, IoT, and other AI-driven advancements. Organizations should likewise explore the complex landscape of LPDDR5-related intellectual property, going with informed choices to cultivate advancement while mitigating legal risks. The journey ahead guarantees invigorating improvements at the crossing point of LPDDR5, 5G, and artificial intelligence, with profound implications for innovation and society alike.

Categories
Mechanical

Intellectual Property and ChatGPT: Navigating the Ethical Landscape

Chatgpt, intellectual property

As cutting-edge artificial intelligence chatbots become progressively modern, they are bringing up significant questions about IPR law and its application to these new advances. Specifically, there are worries about the ownership of content produced by artificial intelligence chatbots, and how to protect and manage the content made by AI.

One main point of interest is the degree to which artificial intelligence chatbots can be thought of as “creators” of original content for reasons of copyright regulation. As these frameworks become further developed, they can produce even better pictures, texts, and different types of content that are indistinguishable from content made by humans. This brings up issues about who should be thought of as the “creator” of the substance for copyright, and whether such content ought to be qualified to be given similar IP rights.

As a rule, copyrighted materials are made by human creators and are considered original content that is fixed in a substantial form. This implies that the work should be communicated in a physical or computerized form, like a book, a PC file, or a painting, to be safeguarded by intellectual property law. With regards to artificial intelligence chatbots, it is not clear whether the substance produced by these frameworks would be viewed as original and fixed in a substantial form, and consequently qualified for copyright protection law.

Cheap and cheerful: why ChatGPT is no trademark filer | Managing Intellectual Property

Some might contend that artificial intelligence is simply a tool or instrument that is utilized by human creators for work, and subsequently, the human creator ought to be viewed as the original maker and proprietor of the work. Others might contend that computer-based intelligence itself ought to be viewed as the maker and proprietor of the work, provided its capacity to produce unique substance without any intervention by a human.

It is challenging to say for certain whether the substance produced by computer-based intelligence would be qualified for copyright law under existing regulations. Nonetheless, the rise of these advancements brings up significant questions and difficulties that should be addressed to guarantee that IP rights are safeguarded.

Another issue is the potential for IP infringement by artificial intelligence chatbots. As these frameworks become all the more broadly utilized, there is a gamble that they may coincidentally or purposefully produce content that encroaches on the Intellectual Property rights of others or that is duplicative of other artificial intelligence-created content. For instance, an AI chatbot that produces text or pictures in light of previous work without consent could be considered encroaching.

The development of cutting-edge artificial intelligence devices raises significant concerns related to IP that should be addressed to guarantee that these innovations are utilized ethically and that respect the rights of human creators. Technologists, attorneys, and policymakers should cautiously consider these issues and work together to foster fitting legal structures for the utilization of artificial intelligence in the production of original content.