Related papers: Applying Machine Learning Analysis for Software Qu…

Understanding how software defects manifest and evolve in production environments is critical for improving reliability. While previous research has largely focused on pre-release defects, the nature of residual faults, i.e., those escaping…

Thousands of security vulnerabilities are discovered in production software each year, either reported publicly to the Common Vulnerabilities and Exposures database or discovered internally in proprietary code. Vulnerabilities often…

Machine learning has become prevalent across a wide variety of applications. Unfortunately, machine learning has also shown to be susceptible to deception, leading to errors, and even fatal failures. This circumstance calls into question…

Background: Machine Learning (ML) systems rely on data to make predictions, the systems have many added components compared to traditional software systems such as the data processing pipeline, serving pipeline, and model training. Existing…

In the last couple of years we have witnessed an enormous increase of machine learning (ML) applications. More and more program functions are no longer written in code, but learnt from a huge amount of data samples using an ML algorithm.…

Software product quality can be defined as the features and characteristics of the product that meet the user needs. The quality of any software can be achieved by following a well defined software process. These software process results…

Each year, thousands of software vulnerabilities are discovered and reported to the public. Unpatched known vulnerabilities are a significant security risk. It is imperative that software vendors quickly provide patches once vulnerabilities…

This research describes the initial effort of building a prediction model for defects in system testing carried out by an independent testing team. The motivation to have such defect prediction model is to serve as early quality indicator…

Software engineering (SE) is a dynamic field that involves multiple phases all of which are necessary to develop sustainable software systems. Machine learning (ML), a branch of artificial intelligence (AI), has drawn a lot of attention in…

Bug severity prediction is important in software maintenance, because it helps the development teams to prioritize bugs that have a significant impact on the operation, stability and security of the system. In large software projects bug…

Software defect prediction using code metrics has been extensively researched over the past five decades. However, prediction harnessing non-software metrics is under-researched. Considering that the root cause of software defects is often…

Software estimation is one of the most important activities in the software project. The software effort estimation is required in the early stages of software life cycle. Project Failure is the major problem undergoing nowadays as seen by…

Software engineering is continuously facing the challenges of growing complexity of software packages and increased level of data on defects and drawbacks from software production process. This makes a clarion call for inventions and…

Accurate software defect prediction could help software practitioners allocate test resources to defect-prone modules effectively and efficiently. In the last decades, much effort has been devoted to build accurate defect prediction models,…

Machine learning is nowadays a standard technique for data analysis within software applications. Software engineers need quality assurance techniques that are suitable for these new kinds of systems. Within this article, we discuss the…

Predictive maintenance (PdM) is a concept, which is implemented to effectively manage maintenance plans of the assets by predicting their failures with data driven techniques. In these scenarios, data is collected over a certain period of…

Software reliability is an important quality attrib-ute, often evaluated as either a function of time or of system structures. The goal of this study is to have this metric cover both for component-based software, be-cause its reliability…

Software vulnerability detection is critical in software security because it identifies potential bugs in software systems, enabling immediate remediation and mitigation measures to be implemented before they may be exploited. Automatic…

Defect prevention is the most vital but habitually neglected facet of software quality assurance in any project. If functional at all stages of software development, it can condense the time, overheads and wherewithal entailed to engineer a…

Debugging is considered as a rigorous but important feature of software engineering process. Since more than a decade, the software engineering research community is exploring different techniques for removal of faults from programs but it…