This paper focuses on an industrial experience with software product lines of analytics-enabled solutions, specifically the evolution of the software product line architecture for a Subject Matter Expert Workbench toolset which supports analytic plugins for multiple software product lines. As context, the toolset product line was intended for integration of expert knowledge into a family of industrial asset health applications at runtime. The toolset architecture is now being evolved to build and manage plugins for multiple Industrial Analytics solutions (software systems and services) beyond asset health. This evolution is driving changes in the desired architecture qualities of the toolset; widening the stakeholder pool and influencing priorities; affecting the architecture tradeoffs and decisions; and triggering updates to the product line architecture, the guidance for applying it, and the current prototype of the toolset. We describe our experiences in handling this evolution, assess lessons learned, and discuss potential relevance to other product line scenarios.

Advanced asset health management solutions blend business intelligence with analytics that incorporate expert operational knowledge of industrial equipment and systems. Key challenges in developing these solutions include: streamlining the capture and prepackaging of operational experts' knowledge as analytic modules, efficiently evolving the modules as knowledge grows, adapting the analytics in the field for diverse operating circumstances and industries, and executing the analytics with high performance in industrial and enterprise software systems. A Quality Attribute Workshop (QAW) was used to elicit and analyze variability at development time and runtime for creating, integrating, evolving, and tailoring reusable analytic modules for ABB/Ventyx asset health solution offerings. Dynamic software product line (DSPL) architecture approaches were then applied in designing an analytics plug in architecture for asset health solutions. This paper describes our approach and experiences in designing the analytics product line architecture and its SME Workbench toolset, and how we achieved significant improvements in speed and flexibility of deploying industrial analytics.

Globally distributed software development (GSD) is increasing in popularity in industry. However, as it is coupled with challenges of distance, time, and culture, it increases the importance of identifying and understanding the specific factors that enable and hinder GSD teams. This paper presents the approach and preliminary findings from an exploratory study of the enabling and inhibiting factors that affected several globally distributed projects in a large commercial organization. Our quantitative analysis includes grouping these factors to reduce the dimensional complexity, studying their underlying causal relationships, and identifying the most influential factors using factor analysis and structural equation modeling. The paper concludes by presenting preliminary findings, limitations, and directions for future work.

ABB incorporates a variety of commercial-off-the-shelf (COTS) components in its products. When new releases of these components are made available for integration and testing, source code is often not provided. Various regression test selection processes have been developed and have been shown to be cost effectiveness. However, the majority of these test selection techniques rely on access to source code for change identification. In this paper we present the application of the lightweight Integrated - Black-box Approach for Component Change Identification (I-BACCI) Version 3 process that select regression tests for applications that use COTS components. Two case studies, examining a total of nine new component releases, were conducted at ABB on products written in C/C++ to determine the effectiveness of I-BACCI. The results of the case studies indicate this process can reduce the required number of regression tests at least 70% without sacrificing the regression fault exposure.

Various regression test selection techniques have been developed and have shown to improve testing cost effectiveness via improving efficiency. The majority of these test selection techniques rely on access to source code for change identification. However, when new releases of COTS components are made available for integration and testing, source code is often not available to guide in regression test selection. In this paper we describe a lightweight integrated-black-box approach for component change identification (I-BACCI) process for selection of regression tests for user/glue code that uses COTS components. I-BACCI is applicable when component licensing agreements do not preclude binary code analysis. A case study of the process was conducted on an ABB product that uses a medium-scale internal ABB software component. Six releases of the component were examined to evaluate the efficacy of the proposed process. The result of the case study indicates that this process can reduce the required regression tests by 40% on average.