Related papers: Are Machine Programming Systems using Right Source…
Open-source repositories provide wealth of information and are increasingly being used to build artificial intelligence (AI) based systems to solve problems in software engineering. Open-source repositories could be of varying quality…
Machine programming (MP) is concerned with automating software development. According to studies, software engineers spend upwards of 50% of their development time debugging software. To help accelerate debugging, we present MP-CodeCheck…
Context: In the realm of software development, maintaining high software quality is a persistent challenge. However, this challenge is often impeded by the lack of comprehensive understanding of how specific code modifications influence…
Software debugging has been shown to utilize upwards of half of developers' time. Yet, machine programming (MP), the field concerned with the automation of software (and hardware) development, has recently made strides in both research and…
Software engineering and information systems practices seek ultimately to create the flawless product. One of the tools used to improve the quality of software development is the use of metrics. In this paper, metrics retrieved from open…
Software development projects involve the use of a wide range of tools to produce a software artifact. Software repositories such as source control systems have become a focus for emergent research because they are a source of rich…
This research paper aims to find, analyze and understand code patterns in any software system and measure its quality by defining standards and proposing a formula for the same. Every code that is written can be divided into different code…
Software code quality is a construct with three dimensions: maintainability, reliability, and functionality. Although many firms have incorporated code quality metrics in their operations, evaluating these metrics still lacks consistent…
In recent years, defect prediction has received a great deal of attention in the empirical software engineering world. Predicting software defects before the maintenance phase is very important not only to decrease the maintenance costs but…
Applying DevOps practices to machine learning system is termed as MLOps and machine learning systems evolve on new data unlike traditional systems on requirements. The objective of MLOps is to establish a connection between different…
Metamorphic testing (TM) examines the relations between inputs and outputs of test runs. These relations are known as metamorphic relations (MR). Currently, MRs are handpicked and require in-depth knowledge of the System Under Test (SUT),…
This literature focuses on doing a comparative analysis between Modular Audio Recognition Framework (MARF) and the General Intentional Programming System (GIPSY) with the help of different software metrics. At first, we understand the…
Software quality is considered as one of the most important challenges in software engineering. It has many dimensions which differ from users' point of view that depend on their requirements. Therefore, those dimensions lead to difficulty…
Measurement is an important criterion to improve the performance of a product. This paper presents a comparative study involving measurements between two frameworks MARF and GIPSY. Initially it establishes a thorough understanding of these…
In software development, the identification of source code file experts is an important task. Identifying these experts helps to improve software maintenance and evolution activities, such as developing new features, code reviews, and bug…
Software fault prediction model are employed to optimize testing resource allocation by identifying fault-prone classes before testing phases. Several researchers' have validated the use of different classification techniques to develop…
Energy efficiency has become a growing concern in software development, leading to the need for tools designed to measure energy consumption. While several energy measurement tools are available as open-source projects, their…
This paper illustrates an empirical study of the working efficiency of machine learning techniques in classifying code review text by semantic meaning. The code review comments from the source control repository in GitHub were extracted for…
The analysis of source code through machine learning techniques is an increasingly explored research topic aiming at increasing smartness in the software toolchain to exploit modern architectures in the best possible way. In the case of…
Context. Source code refactoring is a well-established approach to improving source code quality without compromising its external behavior. Motivation. The literature described the benefits of refactoring, yet its application in practice…