Helena Holmström Olsson

The Early Stage Software Startup Development Model: A Framework for Operationalizing Lean Principles in Software Startups

Software startups are more popular than ever and growing in numbers. They operate under conditions of extreme uncertainty and face many challenges. Often, agile development practices and lean principles are suggested as ways to increase the odds of succeeding as a startup, as they both advocate close customer collaboration and short feedback cycles focusing on delivering direct customer value. However, based on an interview study we see that despite guidance and support in terms of well-known and documented development methods, practitioners find it difficult to implement and apply these in practice. To explore this further, and to propose operational support for software startup companies, this study aims at investigating (1) what are the typical challenges when finding a product idea worth scaling, and (2) what solution would serve to address these challenges. To this end, we propose the ‘Early Stage Software Startup Development Model’ (ESSSDM). The model extends already existing lean principles, but offers novel support for practitioners for investigating multiple product ideas in parallel, for determining when to move forward with a product idea, and for deciding when to abandon a product idea. The model was evaluated in a software startup project, as well as with industry professionals within the software startup domain.

Towards Data-Driven Product Development: A Multiple Case Study on Post-deployment Data Usage in Software-Intensive Embedded Systems

Today, products within telecommunication, transportation, consumer electronics, home automation, security etc. involve an increasing amount of software. As a result, organizations that have a tradition within hardware development are transforming to become software-intensive organizations. This implies products where software constitutes the majority of functionality, costs, future investments, and potential. While this shift poses a number of challenges, it brings with it opportunities as well. One of these opportunities is to collect product data in order to learn about product use, to inform product management decisions, and for improving already deployed products. In this paper, we focus on the opportunity to use post-deployment data, i.e. data that is generated while products are used, as a basis for product improvement and new product development. We do so by studying three software development companies involved in large-scale development of embedded software. In our study, we highlight limitations in post-deployment data usage and we conclude that post-deployment data remains an untapped resource for most companies. The contribution of the paper is two-fold. First, we present key opportunities for more effective product development based on post-deployment data usage. Second, we propose a framework for organizations interested in advancing their use of post-deployment product data.

Unrestored Dentin Caries and Deep Dentin Restorations in Swedish Adolescents

The objectives of this longitudinal study were to assess: (a) the prevalence of unrestored dentin caries among 15-year-olds, (b) the proportion of these lesions that had progressed to deep dentin lesions (inner half of dentin) since the immediately preceding examination at the age of 14 and (c) the frequency of deep restorations (extending into the inner half of the dentin). The sample consisted of all 15-year-olds (n = 2,487) born in 1990 and included in the Public Dental Service in Malmö, Sweden. Bitewing radiographs taken during 2005–2007 and the immediately preceding radiographs were analysed and scored by two examiners. The main radiographic scores were: sound; radiolucency in the outer or inner half of dentin; restored surface. The results showed that 22% of the individuals had 1 or more dentin lesions left unrestored from the time of the examination at the age of 14 until the next recall examination at the age of 15. During the observation period (median time 1.2 years), 9% of the unrestored outer dentin lesions progressed to deep dentin lesions. The majority of these (93%) were in molars. One or more deep restorations were found in 22% of the 15-year-olds; the majority involved occlusal surfaces of first molars. In conclusion, unrestored dentin lesions were common in 15-year-olds. Progression to deep dentin lesions occurred in 9% of these lesions and was most common in first molars. Occlusal surfaces of first molars had the highest frequency of deep restorations.

Post-deployment Data Collection in Software-Intensive Embedded Products

Companies collect terabytes of data about their products in the field, but research shows that R&D makes little use of this data, i.e., it is an untapped resource. In this paper, we explore collection and usage of post-deployment product data. We highlight the existing limitations in post-deployment data usage and the untapped resource that post-deployment product data remains. Based on a multiple case study at three software development companies, we collected three main findings. First, post-deployment data is used as input to the next pre-development phase, but not for improvement of existing product versions. Second, post-deployment data is used for troubleshooting and support activities, but not for innovation of new features. Third, post-deployment data provides a system-level understanding of operation and performance, but does not provide insight in individual feature usage. Finally, we propose a framework in which we outline what development practices and organizational mechanisms that need to be in place for advancing the usage of post-deployment data and advance the development of software-intensive embedded products.

Customer Feedback and Data Collection Techniques in Software R&D: A Literature Review

In many companies, product management struggles in getting accurate customer feedback. Often, validation and confirmation of functionality with customers takes place only after the product has been deployed, and there are no mechanisms that help product managers to continuously learn from customers. Although there are techniques available for collecting customer feedback, these are typically not applied as part of a continuous feedback loop. As a result, the selection and prioritization of features becomes far from optimal, and product deviates from what the customers need. In this paper, we present a literature review of currently recognized techniques for collecting customer feedback. We develop a model in which we categorize the techniques according to their characteristics. The purpose of this literature review is to provide an overview of current software engineering research in this area and to better understand the different techniques that are used for collecting customer feedback.

Towards Continuous Customer Validation: A Conceptual Model for Combining Qualitative Customer Feedback with Quantitative Customer Observation

Software-intensive product companies are becoming increasingly data-driven as can be witnessed by the big data and Internet of Things trends. However, optimally prioritizing customer needs in a mass-market context is notoriously difficult. While most companies use product owners or managers to represent the customer, research shows that the prioritization made is far from optimal. In earlier research, we have coined the term ‘the open loop problem’ to characterize this challenge. For instance, research shows that up to half of all the features in products are never used. This paper presents a conceptual model that emphasizes the need for combining qualitative feedback in early stages of development with quantitative customer observation in later stages of development. Our model is inductively derived from an 18 months close collaboration with six large global software-intensive companies.

Transitioning Manual System Test Suites to Automated Testing: An Industrial Case Study

Visual GUI testing (VGT) is an emerging technique that provides software companies with the capability to automate previously time-consuming, tedious, and fault prone manual system and acceptance tests. Previous work on VGT has shown that the technique is industrially applicable, but has not addressed the real-world applicability of the technique when used by practitioners on industrial grade systems. This paper presents a case study performed during an industrial project with the goal to transition from manual to automated system testing using VGT. Results of the study show that the VGT transition was successful and that VGT could be applied in the industrial context when performed by practitioners but that there were several problems that first had to be solved, e.g. testing of a distributed system, tool volatility. These problems and solutions have been presented together with qualitative, and quantitative, data about the benefits of the technique compared to manual testing, e.g. greatly improved execution speed, feasible transition and maintenance costs, improved bug finding ability. The study thereby provides valuable, and previously missing, contributions about VGT to both practitioners and researchers.

Technical dependency challenges in large-scale agile software development

This qualitative study investigates challenges associated with technical dependencies and their communication. Such challenges frequently occur when agile practices are scaled to large-scale software development. The use of thematic analysis on semi-structured interviews revealed five challenges: planning, task prioritization, knowledge sharing, code quality, and integration. More importantly, these challenges interact with one another and can lead to a domino effect or vicious circle. If an organization struggles with one challenge, it is likely that the other challenges become problematic as well. This situation can have a significant impact on process and product quality. Our recommendations focus on improving planning and knowledge sharing (with practices such as scrum-of-scrums, continuous integration, open space technology) to break the vicious circle, and to reestablish effective communication across teams, which will then enable large-scale companies to achieve the benefits of large-scale agility.

JAutomate: A Tool for System- and Acceptance-test Automation

System- and acceptance-testing are primarily performed with manual practices in current software industry. However, these practices have several issues, e.g. they are tedious, error prone and time consuming with costs up towards 40 percent of the total development cost. Automated test techniques have been proposed as a solution to mitigate these issues, but they generally approach testing from a lower level of system abstraction, leaving a gap for a flexible, high system-level test automation technique/tool. In this paper we present JAutomate, a Visual GUI Testing (VGT) tool that fills this gap by combining image recognition with record and replay functionality for high system-level test automation performed through the system under tests graphical user interface. We present the tool, its benefits compared to other similar techniques and manual testing. In addition, we compare JAutomate with two other VGT tools based on their static properties. Finally, we present the results from a survey with industrial practitioners that identifies test-related problems that industry is currently facing and discuss how JAutomate can solve or mitigate these problems.

Industrial Challenges of Scaling Agile in Mass-Produced Embedded Systems

When individual teams in mechatronic organizations attempt to adopt agile software practices, these practices tend to only affect modules or sub-systems. The short iterations on team level do not lead to short lead-times in launching new or updated products since the overall R&D approach on an organization level is still governed by an overall stage gate or single cycle V-model.