Jabier Martinez

ESPLA: A Catalog of Extractive SPL Adoption Case Studies

Building Software Product Lines (SPLs) from existing artefacts is known as the extractive approach for SPL adoption. The traditional case is that variants are created with ad-hoc reuse (e.g., copy-paste-modify to quickly respond to different customer needs) and practitioners want to reengineer them to an SPL. Several industrial cases have been presented in the literature to motivate the interest of the extraction and many case studies are used to validate methods and techniques for different activities during this adoption process. However, there is no catalog or repository that gather together case studies and artefacts related to extractive SPL adoption. In this paper we present ESPLA, a catalog of Extractive SPL Adoption case studies that aims to foster the advance of this field by providing comprehensive information about case studies that will be otherwise scattered in the literature. Researchers, practitioners and educators can use this catalog to find the case studies that better fit to their particular needs. Currently, ESPLA contains information about 123 case studies and it is intended to be a catalog that can be updated and extended by the community.

Bottom-up technologies for reuse: automated extractive adoption of software product lines

Adopting Software Product Line (SPL) engineering principles demands a high up-front investment. Bottom-Up Technologies for Reuse (BUT4Reuse) is a generic and extensible tool aimed to leverage existing similar software products in order to help in extractive SPL adoption. The envisioned users are 1) SPL adopters and 2) Integrators of techniques and algorithms to provide automation in SPL adoption activities. We present the methodology it implies for both types of users and we present the validation studies that were already conducted. BUT4Reuse tool and source code are publicly available under the EPL license. Website http://but4reuse.github.io Video: https://www.youtube.com/watch?v=pa62Yc9LWyk.

Variability Management and Assessment for User Interface Design

User Interface (UI) design remains an open, wicked, complex and multi-faceted problem, owing to the ever increasing variability of design options resulting from multiple contexts of use, i.e., various end-users, heterogeneous devices and computing platforms, as well as their varying environments. Designing multiple UIs for multiple contexts of use inevitably requires an ever growing amount of time and resources that not all organizations are able to afford. Moreover, UI design choices stand on end-users’ needs elicitation, which are recognized to be difficult to evaluate precisely upfront and which require iterative design cycles. All this complex variability should be managed efficiently to maintain time and resources to an acceptable level. To address these challenges, this article proposes a variability management approach integrated into a UI rapid prototyping process, which involves the combination of Model-Driven Engineering, Software Product Lines and Interactive Genetic Algorithms.

Third International Workshop on Reverse Variability Engineering (REVE 2015), associated with SPLC

Variability management of a product family is the core aspect of Software Product Line Engineering. The adoption of this mature approach requires a high upfront investment before being able to automatically generate product instances based on customer requirements. However, this adoption costs and risks could be reduced with an incremental approach, which mines existing assets and then transitions to full product line engineering. Those existing assets can be for instance similar product variants that were implemented using ad-hoc reuse techniques such as clone-and-own. Hence, there is a great need of bottom-up approaches that extract variability from the artifacts (across all the life cycle) of the legacy product variants and manage the consolidated variability. The REVE workshop series aims to bring together the Reengineering and Software Product Line Engineering communities to address this gap.

Visual guidance for product line configuration using recommendations and non-functional properties

Software Product Lines (SPLs) are a mature approach for the derivation of a family of products using systematic reuse. Different combinations of predefined features enable tailoring the product to fit the needs of each customer. These needs are related to functional properties of the system (optional features) as well as non-functional properties (e.g., performance or cost of the final product). In industrial scenarios, the configuration process of a final product is complex and the tool support is usually limited to check functional properties interdependencies. In addition, the importance of nonfunctional properties as relevant drivers during configuration has been overlooked. Thus, there is a lack of holistic paradigms integrating recommendation systems and visualizations that can help the decision makers. In this paper, we propose and evaluate an interrelated set of visualizations for the configuration process filling these gaps. We integrate them as part of the FeatureIDE tool and we evaluate its effectiveness, scalability, and performance.

VaryLATEX: Learning Paper Variants That Meet Constraints

How to submit a research paper, a technical report, a grant proposal, or a curriculum vitae that respect imposed constraints such as formatting instructions and page limits? It is a challenging task, especially when coping with time pressure. In this work, we present VaryLATEX, a solution based on variability, constraint programming, and machine learning techniques for documents written in LATEX to meet constraints and deliver on time. Users simply have to annotate LATEX source files with variability information, e.g., (de)activating portions of text, tuning figures sizes, or tweaking line spacing. Then, a fully automated procedure learns constraints among Boolean and numerical values for avoiding non-acceptable paper variants, and finally, users can further configure their papers (e.g., aesthetic considerations) or pick a (random) paper variant that meets constraints, e.g., page limits. We describe our implementation and report the results of two experiences with VaryLATEX.

Feature location benchmark with argoUML SPL

Feature location is a traceability recovery activity to identify the implementation elements associated to a characteristic of a system. Besides its relevance for software maintenance of a single system, feature location in a collection of systems received a lot of attention as a first step to re-engineer system variants (created through clone-and-own) into a Software Product Line (SPL). In this context, the objective is to unambiguously identify the boundaries of a feature inside a family of systems to later create reusable assets from these implementation elements. Among all the case studies in the SPL literature, variants derived from ArgoUML SPL stands out as the most used one. However, the use of different settings, or the omission of relevant information (e.g., the exact configurations of the variants or the way the metrics are calculated), makes it difficult to reproduce or benchmark the different feature location techniques even if the same ArgoUML SPL is used. With the objective to foster the research area on feature location, we provide a set of common scenarios using ArgoUML SPL and a set of utils to obtain metrics based on the results of existing and novel feature location techniques.

Towards Estimating and Predicting User Perception on Software Product Variants

Estimating and predicting user subjective perceptions on software products is a challenging, yet increasingly important, endeavour. As an extreme case study, we consider the problem of exploring computer-generated art object combinations that will please the maximum number of people. Since it is not feasible to gather feedbacks for all art products because of a combinatorial explosion of possible configurations as well as resource and time limitations, the challenging objective is to rank and identify optimal art product variants that can be generated based on their average likability. We present the use of Software Product Line (SPL) techniques for gathering and leveraging user feedbacks within the boundaries of a variability model. Our approach is developed in two phases: 1) the creation of a data set using a genetic algorithm and real feedback and 2) the application of a data mining technique on this data set to create a ranking enriched with confidence metrics. We perform a case study of a real-world computer-generated art system. The results of our approach on the arts domain reveal interesting directions for the analysis of user-specific qualities of SPLs.

Towards an Automated Product Line Architecture Recovery: The Apo-Games Case Study

Software Product Line Engineering (SPLE) has been widely adopted for applying systematic reuse in families of systems. Given the high upfront investment required for SPLE adoption, organizations commonly start with more opportunistic reuse approaches (e.g., a single system that they clone and modify). However, maintenance problems appear when managing a large number of similar systems where each of them implements and evolves particular characteristics. One viable solution to solve this issue is to migrate to SPLs using an extractive approach. This initiative, in its early phases, includes the definition of a Product Line Architecture (PLA) supporting the variants derivation and also allowing the customization according to customers needs. Our objective is to provide automatic support in PLA recovery to reduce the time and effort in this process. One of the main issues in the extractive approach is the explosion of the variability in the PLA representation. Our approach is based on identifying the minimum subset of cross-product architectural information for an effective PLA recovery. To evaluate our approach, we applied it in the case of the Apo-Games projects. The experimentation in this real family of systems showed that our automatic approach is able to identify variant outliers and help domain experts to take informed decisions to support PLA recovery.

REVE 2017: 5th International Workshop on REverse Variability Engineering

Software Product Line (SPL) migration remains a challenging endeavour. From organizational issues to purely technical challenges, there is a wide range of barriers that complicates SPL adoption. The workshop REverse Variability Engineering (REVE) aims to foster research about making the most of the two main inputs for SPL migration: 1) domain knowledge and 2) legacy assets. Domain knowledge, usually implicit and spread across an organization, is key to define the SPL scope and to validate the variability model and its semantics. At the technical level, domain expertise is also needed to create or extract the reusable software components. Legacy assets can be, for instance, similar product variants (e.g., requirements, models, source code) that were implemented using ad-hoc reuse techniques such as clone-and-own. More generally, the workshop attracts researchers and practitioners contributing to processes, techniques, tools, or empirical studies related to the automatic, semi-automatic or manual extraction or refinement of SPL assets.