Product Line Engineering is becoming a key practice in many software development environments where complex systems are developed for multiple customers with varying needs. In many business contexts, use cases are the main artifacts for communicating requirements among stakeholders. In such contexts, Product Line (PL) use cases capture variable and common requirements while use case-driven configuration generates Product Specific (PS) use cases for each new customer in a product family. In this paper, we propose, apply, and assess a change impact analysis approach for evolving configuration decisions in PL use case models. Our approach includes: (1) automated support to identify the impact of decision changes on prior and subsequent decisions in PL use case diagrams and (2) automated incremental regeneration of PS use case models from PL use case models and evolving configuration decisions. Our tool support is integrated with IBM Doors. Our approach has been evaluated in an industrial case study, which provides evidence that it is practical and beneficial to analyze the impact of decision changes and to incrementally regenerate PS use case models in industrial settings.

Product Line Engineering (PLE) is becoming a common practice in industry to enhance product quality, to reduce development costs, and to improve time-to-market. At the same time, many development contexts are use case-driven and this strongly influences their requirements engineering and system testing practices. In this PhD project, we aim to achieve automated and effective change management in a product family within the context of use case-driven development and system testing. To this end, we first provide a modeling method for capturing variability information explicitly in Product Line (PL) use case and domain models. Then, we propose an automated configuration approach to automatically generate Product Specific (PS) use case and domain models from PL models. In addition, we plan to provide a change impact analysis approach for PL use case and domain models and automated regression test selection for system test cases derived from PL use case models.

We present PUMConf, a tool for supporting configuration that currently focuses on requirements and enables effective product line management in the context of use case-driven development. By design, it relies exclusively on variability modeling for artifacts that are commonly used in such contexts (i.e., use case diagram, specifications and domain model). For given Product Line (PL) use case and domain models, PUMConf checks the consistency of the models, interactively receives configuration decisions from analysts, automatically checks decision consistency, and generates Product Specific (PS) use case and domain models from the PL models and decisions. It has been evaluated on an industrial case study in the automotive domain.

In this paper, we propose, apply, and assess Product line Use case modeling Method (PUM), an approach that supports modeling variability at different levels of granularity in use cases and domain models. Our motivation is that, in many software development environments, use case modeling drives interactions among stakeholders and, therefore, use cases and domain models are common practice for requirements elicitation and analysis. In PUM, we integrate and adapt existing product line extensions for use cases and introduce some template extensions for use case specifications. Variability is captured in use case diagrams while it is reflected at a greater level of detail in use case specifications. Variability in domain concepts is captured in domain models. PUM is supported by a tool relying on Natural Language Processing (NLP). We applied PUM to an industrial automotive embedded system and report lessons learned and results from structured interviews with experienced engineers.