Looking Over the Research Literature on Software Engineering from 2016 to 2018

This paper carries out a bibliometric analysis to detect (i) what is the most influential research on software engineering at the moment, (ii) where is being published that relevant research, (iii) what are the most commonly researched topics, (iv) and where is being undertaken that research (i.e., in which countries and institutions). For that, 6,365 software engineering articles, published from 2016 to 2018 on a variety of conferences and journals, are examined.This work has been funded by the Spanish Ministry of Science, Innovation, and Universities under Project DPI2016-77677-P, the Community of Madrid under Grant RoboCity2030-DIH-CM P2018/NMT-4331, and grant TIN2016-75850-R from the FEDER funds

A Conceptual Framework for Adapation

This paper presents a white-box conceptual framework for adaptation that promotes a neat separation of the adaptation logic from the application logic through a clear identification of control data and their role in the adaptation logic. The framework provides an original perspective from which we survey archetypal approaches to (self-)adaptation ranging from programming languages and paradigms, to computational models, to engineering solutions

A Conceptual Framework for Adapation

We present a white-box conceptual framework for adaptation. We called it CODA, for COntrol Data Adaptation, since it is based on the notion of control data. CODA promotes a neat separation between application and adaptation logic through a clear identification of the set of data that is relevant for the latter. The framework provides an original perspective from which we survey a representative set of approaches to adaptation ranging from programming languages and paradigms, to computational models and architectural solutions

A Conceptual Framework for Adapation

This paper presents a white-box conceptual framework for adaptation that promotes a neat separation of the adaptation logic from the application logic through a clear identification of control data and their role in the adaptation logic. The framework provides an original perspective from which we survey archetypal approaches to (self-)adaptation ranging from programming languages and paradigms, to computational models, to engineering solutions

Verification of Decision Making Software in an Autonomous Vehicle: An Industrial Case Study

Correctness of autonomous driving systems is crucial as\ua0incorrect behaviour may have catastrophic consequences. Many different\ua0hardware and software components (e.g. sensing, decision making, actuation,\ua0and control) interact to solve the autonomous driving task, leading to a level of complexity that brings new challenges for the formal verification\ua0community. Though formal verification has been used to prove\ua0correctness of software, there are significant challenges in transferring\ua0such techniques to an agile software development process and to ensure\ua0widespread industrial adoption. In the light of these challenges, the identification\ua0of appropriate formalisms, and consequently the right verification\ua0tools, has significant impact on addressing them. In this paper, we\ua0evaluate the application of different formal techniques from supervisory\ua0control theory, model checking, and deductive verification to verify existing\ua0decision and control software (in development) for an autonomous\ua0vehicle. We discuss how the verification objective differs with respect tothe choice of formalism and the level of formality that can be applied.\ua0Insights from the case study show a need for multiple formal methods to\ua0prove correctness, the difficulty to capture the right level of abstraction\ua0to model and specify the formal properties for the verification objectives

Lifecycle Management of Automotive Safety-Critical Over the Air Updates: A Systems Approach

With the increasing importance of Over The Air (OTA) updates in the automotive field, maintaining safety standards becomes more challenging as frequent incremental changes of embedded software are regularly integrated into a wide range of vehicle variants. This necessitates new processes and methodologies with a holistic view on the backend, where the updates are developed and released

Software engineering for AI-based systems: A survey

AI-based systems are software systems with functionalities enabled by at least one AI component (e.g., for image-, speech-recognition, and autonomous driving). AI-based systems are becoming pervasive in society due to advances in AI. However, there is limited synthesized knowledge on Software Engineering (SE) approaches for building, operating, and maintaining AI-based systems. To collect and analyze state-of-the-art knowledge about SE for AI-based systems, we conducted a systematic mapping study. We considered 248 studies published between January 2010 and March 2020. SE for AI-based systems is an emerging research area, where more than 2/3 of the studies have been published since 2018. The most studied properties of AI-based systems are dependability and safety. We identified multiple SE approaches for AI-based systems, which we classified according to the SWEBOK areas. Studies related to software testing and software quality are very prevalent, while areas like software maintenance seem neglected. Data-related issues are the most recurrent challenges. Our results are valuable for: researchers, to quickly understand the state-of-the-art and learn which topics need more research; practitioners, to learn about the approaches and challenges that SE entails for AI-based systems; and, educators, to bridge the gap among SE and AI in their curricula.This work has been partially funded by the “Beatriz Galindo” Spanish Program BEAGAL18/00064 and by the DOGO4ML Spanish research project (ref. PID2020-117191RB-I00)Peer ReviewedPostprint (author's final draft

Model Defects in Evolving Software Product Lines: A Review of Literature

Software products lines (SPLs) are long living systems that undergo several evolutions throughout their lifetime due to many reasons related to technology, strategy, business, etc. These evolutions can be the source of several defects that impact the different artefacts of SPLs, namely requirements, models, architecture and code. Many studies in the literature have dealt with the correction of defects in software product lines, but to our knowledge, no reviews have been carried out to provide an extensive overview of these studies. In this paper, we present a literature review of model defects in software product lines. The purpose of this review is to enumerate the different defects discussed in literature and to present the approaches proposed to detect and correct them. The findings of this review reveal new research leads to explore in this issue