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Requirement prioritization is recognized as an important decision-making activity in requirements engineering and software development. Requirement prioritization is applied to determine which requirements should be implemented and…
Requirements elicitation is an important phase of any software project: the errors in requirements are more expensive to fix than the errors introduced at later stages of software life cycle. Nevertheless, many projects do not devote…
Recent recommender systems started to use rating elicitation, which asks new users to rate a small seed itemset for inferring their preferences, to improve the quality of initial recommendations. The key challenge of the rating elicitation…
It is a long-standing desire of industry and research to automate the software development and testing process as much as possible. In this process, requirements engineering (RE) plays a fundamental role for all other steps that build on…
Proper management of requirements is crucial to successful development software within limited time and cost. Nonfunctional requirements (NFR) are one of the key criteria to derive a comparison among various software systems. In most of…
Refactoring is one of the most important activities in software engineering which is used to improve the quality of a software system. With the advancement of deep learning techniques, researchers are attempting to apply deep learning…
Requirements Engineering Methods (REMs) support Requirements Engineering (RE) tasks, from elicitation, through modeling and analysis, to validation and evolution of requirements. Despite the growing interest to design, validate and teach…
Requirements engineering is a knowledge intensive process and crucial for the success of engineering projects. The field of knowledge-based requirements engineering (KBRE) aims to support engineers by providing knowledge to assist in the…
It is known that user-centered approaches to requirements engineering in general lead to a better suited product for the end-users. LLM4RE provides promising approaches to support the requirements elicitation process (e.g. classification of…
In software engineering processes for machine learning (ML)-enabled systems, integrating and verifying ML components is a major challenge. A prerequisite is the specification of ML component requirements, including models and data, an area…
Deep Learning (DL) is being used nowadays in many traditional Software Engineering (SE) problems and tasks. However, since the renaissance of DL techniques is still very recent, we lack works that summarize and condense the most recent and…
Recent reasoning-first models (e.g., OpenAI o1, DeepSeek R1) have spurred a resurgence of interest in RLVR. Nevertheless, advances are dominated by mathematics (e.g., AIME), with competitive-programming code generation underexplored and…
Deep learning (DL) becomes increasingly pervasive, being used in a wide range of software applications. These software applications, named as DL based software (in short as DL software), integrate DL models trained using a large data corpus…
Natural language processing supported requirements engineering is an area of research and development that seeks to apply NLP techniques, tools and resources to a variety of requirements documents or artifacts to support a range of…
Natural Language Processing (NLP) tools support requirements engineering (RE) tasks like requirements elicitation, classification, and validation. However, they are often developed from scratch despite functional overlaps, and abandoned…
Requirements engineering plays a critical role in developing software systems. One of the most difficult tasks in this process is identifying functional requirements. A critical problem in many projects is missing requirements until late in…
Learning Rate (LR) is an important hyper-parameter to tune for effective training of deep neural networks (DNNs). Even for the baseline of a constant learning rate, it is non-trivial to choose a good constant value for training a DNN.…
Performance is arguably the most crucial attribute that reflects the quality of a configurable software system. However, given the increasing scale and complexity of modern software, modeling and predicting how various configurations can…
Deep Learning (DL) systems are key enablers for engineering intelligent applications due to their ability to solve complex tasks such as image recognition and machine translation. Nevertheless, using DL systems in safety- and…
Large Language Models (LLMs) are finding applications in numerous domains, and Requirements Engineering (RE) is increasingly benefiting from their capabilities to assist with complex, language-intensive tasks. This paper presents a…