Change Impact Analysis for Evolving Configuration Decisions in Product Line Use Case Models

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

PUMConf: A Tool to Configure Product Specific Use Case and Domain Models in a Product Line

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

Applying Product Line Use Case Modeling in an Industrial Automotive Embedded System: Lessons Learned and a Refined Approach

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 successfully applied PUM to an industrial automotive embedded system and report lessons learned and results from structured interviews with experienced engineers

Configuring use case models in product families

In many domains such as automotive and avionics, the size and complexity of software systems is quickly increasing. At the same time, many stakeholders tend to be involved in the development of such systems, which typically must also be configured for multiple customers with varying needs. Product Line Engineering (PLE) is therefore an inevitable practice for such systems. Furthermore, because in many areas requirements must be explicit and traceability to them is required by standards, use cases and domain models are common practice for requirements elicitation and analysis. In this paper, based on the above observations, we aim at supporting PLE in the context of use case-centric development. Therefore, we propose, apply, and assess a use case-driven configuration approach which interactively receives configuration decisions from the analysts to generate Product Specific (PS) use case and domain models. Our approach provides the following: (1) a use case-centric product line modeling method (PUM), (2) automated, interactive configuration support based on PUM, and (3) an automatic generation of PS use case and domain models from Product Line (PL) models and configuration decisions. The approach is supported by a tool relying on Natural Language Processing (NLP), and integrated with an industrial requirements management tool, i.e., IBM Doors. We successfully applied and evaluated our approach to an industrial case study in the automotive domain, thus showing evidence that the approach is practical and beneficial to capture variability at the appropriate level of granularity and to configure PS use case and domain models in industrial settings

Partnership for Research on Ebola VACcination (PREVAC): protocol of a randomized, double-blind, placebo-controlled phase 2 clinical trial evaluating three vaccine strategies against Ebola in healthy volunteers in four West African countries

International audienceAbstract Introduction The Ebola virus disease (EVD) outbreak in 2014–2016 in West Africa was the largest on record and provided an opportunity for large clinical trials and accelerated efforts to develop an effective and safe preventative vaccine. Multiple questions regarding the safety, immunogenicity, and efficacy of EVD vaccines remain unanswered. To address these gaps in the evidence base, the Partnership for Research on Ebola Vaccines (PREVAC) trial was designed. This paper describes the design, methods, and baseline results of the PREVAC trial and discusses challenges that led to different protocol amendments. Methods This is a randomized, double-blind, placebo-controlled phase 2 clinical trial of three vaccine strategies against the Ebola virus in healthy volunteers 1 year of age and above. The three vaccine strategies being studied are the rVSVΔG-ZEBOV-GP vaccine, with and without a booster dose at 56 days, and the Ad26.ZEBOV,MVA-FN-Filo vaccine regimen with Ad26.ZEBOV given as the first dose and the MVA-FN-Filo vaccination given 56 days later. There have been 4 versions of the protocol with those enrolled in Version 4.0 comprising the primary analysis cohort. The primary endpoint is based on the antibody titer against the Ebola virus surface glycoprotein measured 12 months following the final injection. Results From April 2017 to December 2018, a total of 5002 volunteers were screened and 4789 enrolled. Participants were enrolled at 6 sites in four countries (Guinea, Liberia, Sierra Leone, and Mali). Of the 4789 participants, 2560 (53%) were adults and 2229 (47%) were children. Those < 18 years of age included 549 (12%) aged 1 to 4 years, 750 (16%) 5 to 11 years, and 930 (19%) aged 12–17 years. At baseline, the median (25th, 75th percentile) antibody titer to Ebola virus glycoprotein for 1090 participants was 72 (50, 116) EU/mL. Discussion The PREVAC trial is evaluating—placebo-controlled—two promising Ebola candidate vaccines in advanced stages of development. The results will address unanswered questions related to short- and long-term safety and immunogenicity for three vaccine strategies in adults and children. Trial registration ClinicalTrials.gov NCT02876328 . Registered on 23 August 2016