Jan Bosch

Chalmers University of Technology

DevOps has become a widely adopted approach in the software industry, especially among companies developing web-based applications. The main focus of DevOps is to address social and technical bottlenecks along the software flow, from the developers' code changes to delivering these changes to the production environments used by customers. However,...

Context : Process Debt, like Technical Debt, can be a source of short-term benefits but often is harmful in the long term for a software organization. Nonetheless, information about Process Debt is scarce in the current literature. Objective: This paper aims to define Process Debt, describe its occurrence with associated risks, and show examples of...

The role of software product management is key for building, implementing and managing software products. However, although there is prominent research on software product management (SPM) there are few studies that explore how this role is rapidly changing due to digitalization and digital transformation of the software-intensive industry. In this...

For years, research on software ecosystems has focused on the many opportunities for stakeholders to engage in collaborative innovation and creation of new customer value. With a common platform as the basis, companies co-evolve capabilities around a shared set of technologies, knowledge, and skills, to develop new products and services that would...

Fast and reliable wireless communication has become a critical demand in human life. In the case of mission-critical (MC) scenarios, for instance, when natural disasters strike, providing ubiquitous connectivity becomes challenging by using traditional wireless networks. In this context, unmanned aerial vehicle (UAV) based aerial networks offer a p...

Federated Learning (FL) has emerged as a promising approach for collaborative machine learning, addressing data privacy concerns. However, existing FL platforms and frameworks often present challenges for software engineers in terms of complexity, limited customization options, and scalability limitations. In this paper, we introduce EdgeFL, an edg...

The emergence of platform ecosystems has transformed the business landscape in many industries, giving rise to novel modes of interorganizational cooperation and value co-creation, as well as unconventional challenges. The vast traces of data generated by platform ecosystems makes them ripe for the use of analytics and data-driven methods aimed at...

Data has been quickly becoming as the fuel, the new oil, of growth and prosperity of companies in the modern age. With useful data and sufficient tools, companies have the ability to enhance their current products, presents new innovations and services as well as generate new revenue streams with a secondary customer base. While there are ongoing e...

Continuous deployment has been practiced for many years by companies developing web‐ and cloud‐based applications. To succeed with continuous deployment, these companies have a strong collaboration culture between the operations and development teams. In addition, these companies use AI, analytics, and big data to assist with time‐consuming postdep...

For companies developing web-based applications, the Dev and the Ops refer to different groups with either operational or development focus. Therefore, DevOps help these companies streamline software development and operations activities by emphasizing the collaboration between the two groups. However, for companies producing software-intensive pro...

Systems-of-systems (SoS) have become increasingly complex and frequently used in highly distributed, dynamic, and open environments. SoS refer to evolving software systems in which constituent systems (themselves systems in their own right) cooperatively work to fulfill specific, complex missions, facing Software Engineering researchers and practit...

Artificial intelligence is increasingly becoming important to businesses because many companies have realized the benefits of applying machine learning (ML) and deep learning (DL) in their operations. ML and DL have become attractive technologies for organizations looking to automate repetitive tasks to reduce manual work and free up resources for...

Artificial Intelligence (AI) engineering is an engineering discipline that is concerned with all aspects of development and evolution of AI systems (that is, systems that include AI components). AI engineering is primarily an extension of software engineering, but it also includes methods and technologies from data science and AI in general.

Data has been claimed to be the new oil of the 21st century as it has seen to be able both to improve the existing products and services as well as to create new revenue streams for its utilizing company with a secondary customers base. However, while there is active streams of research for developing machine learning and data science methods, cons...

Software development companies are increasingly aiming to become data-driven by trying to continuously experiment with the products used by their customers. Although familiar with the competitive edge that the A/B testing technology delivers, they seldom succeed in evolving and adopting the methodology. In this paper, and based on an exhaustive and...

Agile software development is well-known for its focus on close customer collaboration and customer feedback. In emphasizing flexibility, efficiency and speed, agile practices have led to a paradigm shift in how software is developed. However, while agile practices have succeeded in involving the customer in the development cycle, there is an urgen...

Artificial intelligence (AI) and machine learning (ML) are increasingly broadly adopted in industry, However, based on well over a dozen case studies, we have learned that deploying industry-strength, production quality ML models in systems proves to be challenging. Companies experience challenges related to data quality, design methods and process...

The term artificial intelligence (AI) triggers many things in terms of its inherent meaning and potential. The notion of a machine with the same level of intellect as a human or even far exceeding it is enthralling and scary at the same time. Several science fiction movies build on the HAL 9000 or Terminator theme of artificial intelligence bent on...

Large software companies need to support continuous and fast delivery of customer value both in the short and long term. However, this can be hindered if both the evolution and maintenance of existing systems are hampered by Technical Debt. Although a lot of theoretical work on Technical Debt has been produced recently, its practical management lac...

Continuous Integration is a software practice where developers integrate frequently, at least daily. While this is an ostensibly simple concept, it does leave ample room for interpretation: what is it the developers integrate with, what happens when they do, and what happens before they do? These are all open questions with regards to the details o...

Continuous experimentation (CE) refers to a group of practices used by software companies to rapidly assess the usage, value and performance of deployed software using data collected from customers and the deployed system. Despite its increasing popularity in the development of web-facing applications, CE has not been discussed in the development p...

Context: Exploratory testing plays an important role in the continuous integration and delivery pipelines of large-scale software systems, but a holistic and structured approach is needed to realize efficient and effective exploratory testing. Objective: This paper seeks to address the need for a structured and reliable approach by providing a tang...

Randomised field experiments, such as A/B testing, have long been the gold standard for evaluating software changes. In the automotive domain, running randomised field experiments is not always desired, possible, or even ethical. In the face of such limitations, we develop a framework BOAT (Bayesian causal modelling for ObvservAtional Testing), uti...

Deep learning (DL) based software systems are difficult to develop and maintain in industrial settings due to several challenges. Data management is one of the most prominent challenges which complicates DL in industrial deployments. DL models are data-hungry and require high-quality data. Therefore, the volume, variety, velocity, and quality of da...

Continuous deployment has become a widely used practice in web-based software applications. Deploying a new software version to production is a seamless automated process executed thousands of times per day. Continuous deployment reduces the time between a code commit and that commit is active in production. While continuous deployment promises man...

Deep learning (DL) based software systems are difficult to develop and maintain in industrial settings due to several challenges. Data management is one of the most prominent challenges which complicates DL in industrial deployments. DL models are data-hungry and require high-quality data. Therefore, the volume, variety, velocity, and quality of da...

Fast and reliable wireless communication has become a critical demand in human life. When natural disasters strike, providing ubiquitous connectivity becomes challenging by using traditional wireless networks. In this context, unmanned aerial vehicle (UAV) based aerial networks offer a promising alternative for fast, flexible, and reliable wireless...

Artificial intelligence (AI) and the use of machine learning (ML) and deep learning (DL) technologies are becoming increasingly popular in companies. These technologies enable companies to leverage big quantities of data to improve system performance and accelerate business development. However, despite the appeal of ML/DL, there is a lack of syste...

Technical Debt requires the management of several technical and non-technical aspects: process, organization, tools, etc. Software companies must identify where TD is not well managed and where to improve. Based on ten years of research and practice, we have created an effective approach, TD Pulse, to assess Technical Debt Management in large softw...

Fast and reliable connectivity is essential to enhancing situational awareness and operational efficiency for public safety mission-critical (MC) users. In emergency or disaster circumstances, where existing cellular network coverage and capacity may not be available to meet MC communication demands, deployable-network-based solutions such as cells...

Federated Learning, as a distributed learning technique, has emerged with the improvement of the performance of IoT and edge devices. The emergence of this learning method alters the situation in which data must be centrally uploaded to the cloud for processing and maximizes the utilization of edge devices’ computing and storage capabilities. The l...

In recent years, the application of artificial intelligence (AI) has become an integral part of a wide range of areas, including software engineering. By analyzing various data sources generated in software engineering, it can provide valuable insights into customer behavior, product performance, bugs and errors, and many more. In practice, however...

Context : When developing software, it is vitally important to keep the level of technical debt down since, based on several studies, it has been well established that technical debt can lower the development productivity, decrease the developers' morale and compromise the overall quality of the software, among others. However, even if researchers...

Continuous experimentation (CE) refers to a set of practices used by software companies to rapidly assess the usage, value, and performance of deployed software using data collected from customers and systems in the field using an experimental methodology. However, despite its increasing popularity in developing web‐facing applications, CE has not...

Randomised field experiments, such as A/B testing, have long been the gold standard for evaluating the value that new software brings to customers. However, running randomised field experiments is not always desired, possible or even ethical in the development of automotive embedded software. In the face of such restrictions, we propose the use of...

Data collected from in-service products play an important role in enabling software-intensive embedded systems suppliers to embrace data-driven practices. Data can be used in many different ways such as to continuously learn and improve the product, enhance post-deployment services, reduce operational cost or create a better user experience. While...

Continuous Deployment (CD) advocates for quick and frequent deployments of software to production. The goal is to bring new functionality as early as possible to users while learning from their usage. CD has emerged from web-based applications where it has been gaining traction over the past years. While CD is appealing for many software developmen...

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 co...

Data pipelines play an important role throughout the data management process whether these are used for data analytics or machine learning. Data-driven organizations can make use of data pipelines for producing good quality data applications. Moreover, data pipelines ensure end-to-end velocity by automating the processes involved in extracting, tra...

A/B testing is gaining attention in the automotive sector as a promising tool to measure casual effects from software changes. Different from the web-facing businesses, where A/B testing has been well-established, the automotive domain often suffers from limited eligible users to participate in online experiments. To address this shortcoming, we pr...

A/B experimentation is a known technique for data-driven product development and has demonstrated its value in web-facing businesses. With the digitalisation of the automotive industry, the focus in the industry is shifting towards software. For automotive embedded software to continuously improve, A/B experimentation is considered an important tec...

Frequentist statistical methods, such as hypothesis testing, are standard practices in studies that provide benchmark comparisons. Unfortunately, these methods have often been misused, e.g., without testing for their statistical test assumptions or without controlling for family-wise errors in multiple group comparisons, among several other problem...

Benchmark suites, i.e. a collection of benchmark functions, are widely used in the comparison of black-box optimization algorithms. Over the years, research has identified many desired qualities for benchmark suites, such as diverse topology, different difficulties, scalability, representativeness of real-world problems among others. However, while...

With the development and the increasing interests in ML/DL fields, companies are eager to utilize these methods to improve their service quality and user experience. Federated Learning has been introduced as an efficient model training approach to distribute and speed up time-consuming model training and preserve user data privacy. However, common...