Daniela Lettner

What is a feature?: a qualitative study of features in industrial software product lines

The notion of features is commonly used to describe the functional and non-functional characteristics of a system. In software product line engineering, features often become the prime entities of software reuse and are used to distinguish the individual products of a product line. Properly decomposing a product line into features, and correctly using features in all engineering phases, is core to the immediate and long-term success of such a system. Yet, although more than ten different definitions of the term feature exist, it is still a very abstract concept. Definitions lack concrete guidelines on how to use the notion of features in practice. To address this gap, we present a qualitative empirical study on actual feature usage in industry. Our study covers three large companies and an in-depth, contextualized analysis of 23 features, perceived by the interviewees as typical, atypical (outlier), good, or bad representatives of features. Using structured interviews, we investigate the rationales that lead to a features perception, and identify and analyze core characteristics (facets) of these features. Among others, we find that good features precisely describe customer-relevant functionality, while bad features primarily arise from rashly executed processes. Outlier features, serving unusual purposes, are necessary, but do not require the full engineering process of typical features.

A case study on software ecosystem characteristics in industrial automation software

In software ecosystems (SECOs) both internal and external developers build software solutions for specific market segments based on common technological platforms. Despite a significant body of research on SECOs there is still a need to empirically investigate the characteristics of SECOs in specific industrial environments to understand and improve development processes. In particular, when defining software processes understanding the roles of the participants in the SECO is crucial. This paper thus reports results of an exploratory case study in the industrial automation domain. We explore two research questions on SECO characteristics and discuss research issues we derived from our analyses. While our study confirms key SECO characteristics reported in the literature we also identify additional properties relevant for development processes in the domain of industrial automation.

Custom-developed vs. model-based configuration tools: experiences from an industrial automation ecosystem

High demands regarding the variability of automation software motivate organizations to automate the configuration process. In practice, this often leads to the development of custom configuration tools designed specifically for configuring the automation software they were developed for. This approach works well as long as both, the development of the software and the configurator are under the full control of the organization. However, software platforms are increasingly open, i.e., key customers add capabilities and thereby change the platforms variability. Often, these customers create a new platform themselves, which they offer to their customers. Moving from a closed platform to a software ecosystem means that development and variability management happen at multiple layers involving multiple teams with different backgrounds. This poses new requirements regarding the flexibility of configuration tools. In this paper, we report experiences and issues with a custom-developed configurator currently in use in an industrial automation software ecosystem. We describe how a model-based tool can be applied to address these issues and provide a scenario-based comparison of the custom-developed solution and the model-based configurator.

Applying a consistency checking framework for heterogeneous models and artifacts in industrial product lines

Product line engineering relies on heterogeneous models and artifacts to define and implement the product lines reusable assets. The complexity and heterogeneity of product line artifacts as well as their interdependencies make it hard to maintain consistency during development and evolution, regardless of the modeling approaches used. Engineers thus need support for detecting and resolving inconsistencies within and between the various artifacts. In this paper we present a framework for checking and maintaining consistency of arbitrary product line artifacts. Our approach is flexible and extensible regarding the supported artifact types and the definition of constraints. We discuss tool support developed for the DOPLER product line tool suite. We report the results of applying the approach to sales support applications of industrial product lines.

Using regression testing to analyze the impact of changes to variability models on products

Industrial product lines are typically maintained for a long time and evolve continuously to address changing requirements and new technologies. Already derived products often have to be re-derived after such changes to benefit from new and updated features. Product line engineers thus frequently need to analyze the impact of changes to variability models to prevent unexpected changes of re-derived products. In this paper we present a tool-supported approach that informs engineers about the impacts of variability model changes on existing products. Regression tests are used to determine whether existing product configurations and generated product outputs can be re-derived without unexpected effects. We evaluate the feasibility of the approach based on changes observed in a real-world software product line. More specifically, we show how our approach helps engineers performing specific evolution tasks to analyze the change impacts on existing products. We also evaluate the performance and scalability of our approach. Our results show that variability change impact analyses can be automated using model regression testing and can help reducing the gap between domain engineering and application engineering.

Software Evolution in an Industrial Automation Ecosystem: An Exploratory Study

In software ecosystems (SECOs) both internal and external engineers develop software solutions for specific market segments and customers based on common technological platforms. SECOs pose new challenges for software engineering as the platforms are evolved by different development teams and communities. Despite a significant body of research only few empirical results are available on software evolution in SECOs. This paper reports results of an exploratory case study on change characteristics in an industrial automation SECO. We apply Buckley et al.s framework of software change to characterize evolution in an industrial automation SECO. We further discuss evolution challenges we derived from our analyses.

Recovering Feature-to-Code Mappings in Mixed-Variability Software Systems

Software engineering methods for analyzing and managing variable software systems rely on accurate feature-to-code mappings to relate high-level variability abstractions, such as features or decisions, to locations in the code where variability occurs. Due to the continuous and long-term evolution of many systems such mappings need to be extracted and updated automatically. However, current approaches have limitations regarding the analysis of highly-configurable systems that rely on different variability mechanisms. We present a novel approach that exploits the synergies between program analysis and doffing techniques to reveal feature-to-code mappings for highly-configurable systems. We demonstrate the feasibility of our approach with a set of products from a real-world product line in the domain of industrial automation.

Feature modeling of two large-scale industrial software systems: Experiences and lessons learned

Feature models are frequently used to capture the knowledge about configurable software systems and product lines. However, feature modeling of large-scale systems is challenging as many models are needed for diverse purposes. For instance, feature models can be used to reflect the perspectives of product management, technical solution architecture, or product configuration. Furthermore, models are required at different levels of granularity. Although numerous approaches and tools are available, it remains hard to define the purpose, scope, and granularity of feature models. In this paper we thus present experiences of developing feature models for two large-scale industrial automation software systems. Specifically, we extended an existing feature modeling tool to support models for different purposes and at multiple levels. We report results on the characteristics and modularity of the feature models, including metrics about model dependencies. We further discuss lessons learned during the modeling process.

Using Feature Feeds to Improve Developer Awareness in Software Ecosystem Evolution

In many domains organizations need to serve a mass market while at the same time customers request highly individual solutions. Companies thus form software ecosystems (SECOs) comprising various related hardware and software product lines (SPLs). Technology changes, internal enhancements, and customer requests drive the evolution of such SECOs. Multiple projects are conducted in parallel to deliver customized solutions to customers. Developers often adhere to a staged configuration process: first, required software components are selected to derive an initial product, which is then evolved by refining features and adapting source code to meet customer requirements. These customer-specific solutions are often created using a clone-and-own approach and typically contain features potentially reusable in other solutions. However, the awareness of developers about such platform extensions is typically low and feedback from products to SPLs is often lacking. In this research-in-progress paper we thus present a publish-subscribe approach fostering the awareness about feature implementations in SECOs. The approach is based on feature feeds and SECO awareness models.

A Case Study on the Evolution of a Component-based Product Line

Product line engineering is an approach that works well for managing the anticipated variability of software systems as demonstrated in numerous studies. However, little empirical research and few approaches exist for dealing with the unanticipated evolution of product lines. As a result, the understanding of product line evolution is still weak and the maturity of approaches and tools supporting evolution is often insufficient. In this paper we present results of a case study on impact analyses and desired tool support in product line evolution. Our findings are based on observing 30 person months of development. We analyzed changes made to a product line in typical evolution scenarios by involving the key developers. We used empirical data on observed development activities and impact analyses to derive a trace information model showing frequently desired trace links. We discuss lessons learned and implications for tool developers.