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Abstract: The First International Workshop on Requirement Engineering for Software startups and Emerging Technologies (RESET) is a part of IEEE International Requirements Engineering Conference 2021, held on 20th September, 2021. The workshop brought together requirements engineering researchers and practitioners to discuss the need for adapting conventional requirement engineering artifacts (i.e. requireme...
Abstract: In practice, supervised learning algorithms require fully labeled datasets to achieve the high accuracy demanded by current modern applications. However, in industrial settings supervised learning algorithms can perform poorly because of few labeled instances. Semi-supervised learning (SSL) is an automatic labeling approach that utilizes complete labels to infer missing labels in partially complet...
Abstract: Continuous Deployment is the practice to deploy software more frequently to customers and learn from their usage. The aim is to introduce new functionality and features in an additive way to customers as soon as possible. While Continuous Deployment is becoming popular among web and cloud-based software development organizations, the adoption of continuous deployment within the software-intensive ...
Abstract: This position paper presents and illustrates the concept of security requirements as code – a novel approach to security requirements specification. The aspiration to minimize code duplication and maximize its reuse has always been driving the evolution of software development approaches. Object-Oriented programming (OOP) takes these approaches to the state in which the resulting code conceptually...
Abstract: Companies run A/B tests to accelerate innovation and make informed data-driven decisions. At Microsoft alone, over twenty thousand A/B tests are ran each year helping decide which features maximize user value. Not all teams and companies succeed in establishing and growing their A/B testing programs. In this paper, we explore multiple-case studies at Microsoft, Outreach, Booking.com, and empirical...
Abstract: Background: Technical Debt is a consolidated notion in software engineering research and practice. However, the estimation of its impact (interest of the debt) is still imprecise and requires heavy empirical and experimental inquiry. Objective: We aim at developing a data-driven approach to calculate the interest of Technical Debt in terms of delays in resolving affected tasks.Method: We conducted...
Abstract: Architectural technical debt (ATD) may create a substantial extra effort in software development, which is called interest. There is little evidence about whether repaying ATD in microservices reduces such interest. Objectives: We wanted to conduct a first study on investigating the effect of removing ATD on the occurrence of incidents in a microservices architecture. Method: We conducted a quanti...
Abstract: Traceability management relies on a supporting model, the traceability information model (TIM), that defines which types of relationships exist between which artifacts and contains additional constraints such as multiplicities. Constructing a TIM that is fit for purpose is crucial to ensure that a traceability strategy yields the desired benefits. However, which design decisions are critical in th...
Abstract: The MobSTr dataset contains a number of artifacts for an autonomous driver assistance system, ranging from textual requirements to models for system design and models relevant to safety assurance. The artifacts provided are connected with traceability links created and managed with Eclipse Capra, an open source traceability management tool. The dataset builds upon a custom traceability information...
Abstract: Welcome to the First International Workshop on Requirements Engineering for Explainable Systems (RE4ES), where we aim to advance requirements engineering (RE) for explainable systems, foster interdisciplinary exchange, and build a community. On the one hand, we believe that the methods and techniques of the RE community can add much value to explainability research. On the other hand, we have to e...
Abstract: Machine Learning (ML) is an application of Artificial Intelligence (AI) that uses big data to produce complex predictions and decision-making systems, which would be challenging to obtain otherwise. To ensure the success of ML-enabled systems, it is essential to be aware of certain qualities of ML solutions (performance, transparency, fairness), known from a Requirement Engineering (RE) perspectiv...
Abstract: As technology has allowed us to collect large amounts of industrial data, it has become critical to analyze and understand the data collected, in particular to find data anomalies. Anomaly analysis allows a company to detect, analyze and understand anomalous or unusual data patterns. This is an important activity to understand, for example, deviations in service which may indicate potential proble...
Abstract: Collection of manuscripts accepted for presentation at the Student Forum and Fast Abstracts tracks of the 17th European Dependable Computing Conference (EDCC 2021)....
Abstract: Big data and machine learning models have been increasingly used to support software engineering practices. One example is the use of machine learning models to improve test case selection in continuous integration. However, one of the challenges in building such models is the large volume of noise that comes in data, which impedes their predictive performances. In this paper, we address this issu...
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