Artificial Intelligence for Software Engineering: An Initial Review on Software Bug Detection and Prediction

Fadhil, Julanar Ahmed; Koh, Tieng Wei; Kew, Si Na

doi:10.3844/jcssp.2020.1709.1717

Cited by 4 publications

(4 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…If the model's boundaries are not well-defined or it lacks resilience, attackers can generate pictures intentionally tailored to deceive the AI. These altered visuals, known as adversarial examples, may look innocuous to humans, but the AI may misidentify them as threats (Fadhil et al, 2020).…”

Section: The Answer Of Ai Profession Relevancymentioning

confidence: 99%

Quo vadis Indonesian public relations profession: dilemma on the rise of AI

Silviani

2024

Indonesian Journal of Communications Studies

View full text Add to dashboard Cite

The debate over AI has focused on its contrast with human employment. Simple perspectives are inadequate in public communication fields like Public Relations (PR). The conversation must shift from discussing AI's impact on human jobs to how AI will shape public communication as AI becomes more important in PR. To examine AI's effects on Public Relations, this study uses a literature review focusing on authority and public voice. Due to the risks of AI misuse, data discrimination, and data poisoning, public relations must carefully consider the implications of AI integration. Artificial Intelligence (AI) in Public Relations is studied using a literature review as its main method. Experts are consulted to identify AI's most significant challenges and opportunities in public relations. Public relations (PR) are affected by Artificial Intelligence (AI), which presents both opportunities and challenges. AI can boost public communication, but it also risks data misuse and discrimination. Ethical issues arise when AI represents the public's voice. This study provides useful insights into how Indonesia's Public Relations profession should approach Artificial Intelligence. PR professionals can overcome technical and ethical challenges and revolutionise public communication with AI integration. Therefore, ethical PR practice and the use of AI are becoming increasingly important.

show abstract

Section: The Answer Of Ai Profession Relevancymentioning

confidence: 99%

Quo vadis Indonesian public relations profession: dilemma on the rise of AI

Silviani

2024

Indonesian Journal of Communications Studies

View full text Add to dashboard Cite

show abstract

“…Fadhil et al [ 11 ] determined how AI can improve software issue detection and prediction methods. Artificial intelligence has helped identify software issues and predict bugs, as data shows.…”

Section: Related Workmentioning

confidence: 99%

“…As a result, a recommender system is required, which must supply services in accordance with the resemblance of goods. By incorporating user and product data into a collaborative recommendation system, true user preferences can be learned [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Trends in Intelligent and AI-Based Software Engineering Processes: A Deep Learning-Based Software Process Model Recommendation Method

Alshammari

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

In recent years, numerous studies have successfully implemented machine learning strategies in a wide range of application areas. Therefore, several different deep learning models exist, each one tailored to a certain software task. Using deep learning models provides numerous advantages for the software development industry. Testing and maintaining software is a critical concern today. Software engineers have many responsibilities while developing a software system, including coding, testing, and delivering the software to users via the cloud. From this list, it is easy to see that each task calls for extensive organization and preparation, as well as access to a variety of resources. A developer may consult other code repositories, websites with related programming content, and even colleagues for information before attempting to build and test a solution to the problem at hand. In this investigation, we aim to identify the factors that led to developing the recommender. This system analyzes the recommender’s performance and provides suggestions for improving the software based on users’ opinions.

show abstract

“…Early detection of software bugs can reduce development costs, time, rework efforts [3]. SBP serves as a means to track software modules and assess their reliability by examining specific parameter characteristics obtained from software projects [4]. Several machine learning (ML) techniques have been introduced to deal with the SBP problem [5], [6].…”

Section: Introductionmentioning

confidence: 99%

Improving the accuracy of recurrent neural networks models in predicting software bug based on undersampling methods

Khleel,

Nehéz

2023

IJEECS

View full text Add to dashboard Cite

<span>The process of identifying software bugs is of paramount importance as it ensures software reliability and facilitates maintenance activities. The quality improvement process of software relies heavily on software bug prediction (SBP). In SBP, the task of accurately identifying defective source code poses a significant challenge. Numerous of machine learning (ML) models has been developed specifically to address this challenge in SBP. Nonetheless, the class imbalance issue restricts the potential of these models to predict software bugs accurately. This issue poses a significant hindrance to the efficiency of these models, leading to imbalanced false-positive and false-negative outcomes. Previous studies have paid limited attention to addressing the challenge of class imbalance in SBP. This study aims to fill this research gap by employing a combination of two recurrent neural networks (RNNs), namely <a name="_Hlk141433135"></a>long-short-term memory (LSTM) and gated recurrent unit (GRU), along with an undersampling method (near miss) to effectively tackle this issue. Experiments have been conducted on publicly available benchmark datasets, considering both class-level and file-level metrics. The experimental results lead to the conclusion that our models outperform others and the combination of RNNs models with undersampling methods leads to improved bug prediction performance, particularly for datasets with imbalanced class distributions.</span>

show abstract

Artificial Intelligence for Software Engineering: An Initial Review on Software Bug Detection and Prediction

Cited by 4 publications

References 37 publications

Quo vadis Indonesian public relations profession: dilemma on the rise of AI

Quo vadis Indonesian public relations profession: dilemma on the rise of AI

Trends in Intelligent and AI-Based Software Engineering Processes: A Deep Learning-Based Software Process Model Recommendation Method

Improving the accuracy of recurrent neural networks models in predicting software bug based on undersampling methods

Contact Info

Product

Resources

About