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The development and deployment of machine learning (ML) systems can be executed easily with modern tools, but the process is typically rushed and means-to-an-end. The lack of diligence can lead to technical debt, scope creep and misaligned…
Scaling model size, training data, and compute power have driven advances in large language models (LLMs), but these approaches are reaching saturation as human-generated text is exhausted and further gains diminish. We propose experience…
The technology of formal software verification has made spectacular advances, but how much does it actually benefit the development of practical software? Considerable disagreement remains about the practicality of building systems with…
Software development includes diverse tasks such as implementing new features, analyzing requirements, and fixing bugs. Being an expert in those tasks requires a certain set of skills, knowledge, and experience. Several studies investigated…
This paper presents a hybrid approach to predict the evolution of technological maturity in R and D projects, using the oil and gas sector as an example. Integrating System Dynamics (SD) and Agent Based Modelling (ABM) allows the proposed…
Applying software defect esimation techniques and presenting this information in a compact and impactful decision table can clearly illustrate to collaborative groups how critical this position is in the overall development cycle. The Test…
Actually, software products are increasing in a fast way and are used in almost all activities of human life. Consequently measuring and evaluating the quality of a software product has become a critical task for many companies. Several…
The introduction of machine learning (ML) components in software projects has created the need for software engineers to collaborate with data scientists and other specialists. While collaboration can always be challenging, ML introduces…
Artificial intelligence (AI) maturity models have proliferated, yet prevailing frameworks remain largely enterprise-centric, linear, and weakly aligned with the organizational realities of small and medium-sized enterprises (SMEs). This…
The term Model-Driven Engineering (MDE) is typically used to describe software development approaches in which abstract models of software systems are created and systematically transformed to concrete implementations. In this paper we give…
The acceptance of autonomous vehicles is dependent on the rigorous assessment of their safety. Furthermore, the commercial viability of AV programs depends on the ability to estimate the time and resources required to achieve desired safety…
We describe the current state and future plans for a set of tools for scientific data management (SDM) designed to support scientific transparency and reproducible research. SDM has been in active use at our MRI Center for more than two…
Software engineering is a young discipline. Despite efforts in recent years, some elements still require further development, research, and systematization. One of these elements are methods. They consist of a set of well-defined activities…
Predictive modeling has an increasing number of applications in various fields. High demand for predictive models drives creation of tools that automate and support work of data scientist on the model development. To better understand what…
This work proposes the Crypto-Agility Maturity Model (CAMM for short), a maturity model for determining the state of crypto-agility of a given software or IT landscape. CAMM consists of five levels, for each level a set of requirements have…
Software containers are widely adopted for developing and deploying software applications. Despite their popularity, major security concerns arise during container development and deployment. Software Engineering (SE) research literature…
Mentorship in open source software (OSS) is a vital, multifaceted process that includes onboarding newcomers, fostering skill development, and enhancing community building. This study examines task-focused mentoring strategies that help…
The software process model consists of a set of activities undertaken to design, develop and maintain software systems. A variety of software process models have been designed to structure, describe and prescribe the software development…
Machine Learning (ML) has become a ubiquitous tool for predicting and classifying data and has found application in several problem domains, including Software Development (SD). This paper reviews the literature between 2000 and 2019 on the…
The software product line aims at the effective utilization of software assets, reducing the time required to deliver a product, improving the quality, and decreasing the cost of software products. Organizations trying to incorporate this…