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

With the increasing importance of complex software systems in the software industry, the need for using agent technologies to develop large-scale commercial and industrial software systems is growing rapidly. Such systems are complex, and there is a pressing need for system modeling techniques to support reliable, maintainable and extensible design. G-nets are a type of Petri net defined to support the modeling of a system as a set of independent and loosely-coupled modules. In this paper, we first introduce an extension of G-nets - the agent-based G-net - as a generic model for agent design. Then, to progress from an agent-based design model to an agent-oriented model, new mechanisms to support inheritance modeling are introduced. To illustrate our formal modeling technique for multi-agent systems, an example of an agent family in electronic commerce is provided.

Dealing with clones is a common problem in large-scale software projects. While most of the research in this area is focused on detecting, and investigating the reasons of clones, little research has been done on the use of clone technology in large-scale industrial software. In this paper we present an in-depth case study of cloning in Command and Control Software Framework (CCSF) developed by HAVELSAN, a large software company in the Turkish defense industry. We proposed a practical classification schema for clones based on their removal strategies. A detailed analysis on categorization of clones, and lessons learned in clone management of large-scale industrial software systems are presented.

Agents are becoming one of the most important topics in distributed and autonomous decentralized systems, and there are increasing attempts to use agent technologies to develop large-scale commercial and industrial software systems. The complexity of such systems suggests a pressing need for system modeling techniques to support reliable, maintainable, and extensible design. G-nets are a type of Petri net defined to support system modeling in terms of a set of independent and loosely-coupled modules. In this paper, we customize the basic G-net model to define a so-called "agent-based G-net" that can serve as a generic model for agent design. Then, to progress from an agent-based design model to an agent-oriented model, new mechanisms to support inheritance modeling are introduced. To illustrate our formal modeling technique for multiagent systems, an example of an agent family in electronic commerce is provided. Finally, we demonstrate how we can use model checking to verify some key behavioral properties of our agent model. This is facilitated by the use of an existing Petri net tool.

We evaluate the benefits of applying aspect-oriented software development techniques in the context of a large-scale industrial embedded software system implementing a number of crosscutting concerns. Additionally, we assess the feasibility of automatically extracting these crosscutting concerns from the source code. In order to achieve this, we present an approach for reverse engineering aspects from an ordinary application automatically. This approach incorporates both a concern verification and an aspect construction phase. Our results show that such automated support is feasible, and can lead to significant improvements in source code quality.

Needs for mass customization and economies of scale have pushed engineers to develop Software Product Lines (SPLs). SPLs are families of products sharing commonalities and exhibiting differences, built by reusing software assets abstractly represented by features. Feature models describe the constraints that link the features and allow the configuration of tailored software products. Common SPLs involve hundreds, even thousands of features, leading to billions of possible software products. As a result, testing a product line is challenging due to the enormous size of the possible products. Existing techniques focus on testing based on the product line's feature model by selecting a limited set of products to test. Being created manually or reverse-engineered, feature models are prone to errors impacting the generated test suites. In this paper, we examine ability of test suites to detect such errors. In particular, we propose two mutation operators to derive erroneous feature models (mutants) from an original feature model and assess the capability of the generated original test suite to kill the mutants. Experimentation on real feature models demonstrate that dissimilar tests suites have a higher mutant detection ability than similar ones, thus validating the relevance of similarity-driven product line testing.

Feature models are widely used for describing the variability of a software product line. A feature model contains a tree of features and a set of constraints over these features, which define valid feature combinations. In the industrial practice, large feature models containing hundreds of features and constraints are common. Furthermore, in a hierarchical product line a feature model can be related to other feature models through inter-model constraints. Due to the model size and complexity, understanding industrial feature models is a challenging task.In this paper, we describe the feature model understanding challenges reported by feature model developers at Robert Bosch GmbH. To support the developers in model understanding, we extend the idea of a feature implication graph to feature relation graph by abstracting groups of implications to feature relations. A transitively closed relation graph shows all modeled and implicit feature relations and spans all related feature models. The graph is also used to identify modeling problems, such as false optional or dead features, and to show the derivation of any implicit relation or problem from the modeled constraints. In a case study at Bosch, we evaluate the use of feature relation graph for model understanding. We propose further use cases of the graph, supporting model maintenance, evolution and configuration.

When developing large-scale industrial software systems engineers need to instantiate, configure, and deploy diverse reusable components. The number of component instances required depends on customer requirements only known during configuration and is typically unknown when modeling the systems’ variability. Also, the hierarchy of dynamically created component instances leads to complex dependencies between configuration decisions. Dealing with component multiplicity and hierarchy thus requires an approach capable of expressing the dependencies among dynamically instantiated components and configuration decisions. Furthermore, users need tool support for navigating the complex decision space during configuration. In this experience paper we report on applying a decision-oriented modeling approach for defining component variability, multiplicity, and hierarchy. We further present a configuration tool that guides end users through the complex decision space. We report applications of the approach to industrial software systems and describe patterns and lessons learned.

To assure high software quality for large-scale industrial software systems, traditional approaches of software quality assurance, such as software testing and performance engineering, have been widely used within Alibaba, the world's largest retailer, and one of the largest Internet companies in the world. However, there still exists a high demand for software quality assessment to achieve high sustainability of business growth and engineering culture in Alibaba. To address this issue, we develop an industrial solution for software quality assessment by following the GQM paradigm in an industrial setting. Moreover, we integrate multiple assessment methods into our solution, ranging from metric selection to rating aggregation. Our solution has been implemented, deployed, and adopted at Alibaba: (1) used by Alibaba's Business Platform Unit to continually monitor the quality for 60+ core software systems; (2) used by Alibaba's R&D Efficiency Unit to support group-wide quality-aware code search and automatic code inspection. This paper presents our proposed industrial solution, including its techniques and industrial adoption, along with the lessons learned during the development and deployment of our solution.

Trusted Product Lines

Feature models are a de facto standard for representing the commonalities and variability of product lines and configurable software systems. Requirements-level features are commonly implemented in multiple source code artifacts, which results in complex dependencies at the code level. As developers change and evolve features frequently, it is challenging to keep feature models consistent with their implementation. We thus present an approach combining feature-to-code mappings and code dependency analyses to inform engineers about possible inconsistencies. Our focus is on code-level changes requiring updates in feature dependencies and constraints. Our approach uses static code analysis and a variation control system to lift complex code-level dependencies to feature models. We present the suggested dependencies to the engineer in two ways: directly as links between features in a feature model and as a heatmap visualizing the dependency changes of all features in a model. We present results of an evaluation on the Pick-and-Place Unit system, which demonstrates the utility and performance of our approach and the quality of the suggestions.

Automating feature model refactoring: A Model transformation approach

Feature modeling is a key technique for capturing and explicit documenting the software product line variability. Object Oriented (00) concepts have been frequently used to encapsulate and reuse software resources in large scale systems. To improve the possibilities of combination and derivation between distinct feature models, this paper defines the Object Oriented Feature Modeling (OOFM) approach. It is a combination of Object Oriented concepts and Feature Modeling techniques that allows the declaration of feature classes and its derivation methods, and the instantiation of feature objects with their encapsulated informations. As a result, the integration and the production of multiple product lines, by the definition of feature frameworks, combining distinct feature classes according to the domain interest, can be applied.

Feature models are the de facto standard for modelling variabilities and commonalities in features and relationships in software product lines. They are the base artefacts in many engineering activities, such as product configuration, derivation, or testing. Concrete models in different domains exist; however, many are in private or sparse repositories or belong to discontinued projects. The dispersion of knowledge of feature models hinders the study and reuse of these artefacts in different studies. The Universal Variability Language (UVL) is a community effort textual feature model language that promotes a common way of serializing feature models independently of concrete tools. Open science principles promote transparency, accessibility, and collaboration in scientific research. Although some attempts exist to promote feature model sharing, the existing solutions lack open science principles by design. In addition, existing and public feature models are described using formats not always supported by current tools. This paper presents ▪ , a repository of feature models in UVL format. ▪ provides a front end that facilitates the search, upload, storage, and management of feature model datasets, improving the capabilities of discontinued proposals. Furthermore, the tool communicates with Zenodo – one of the most well-known open science repositories – providing a permanent save of datasets and following open science principles. ▪ includes existing datasets and is readily available to include new data and functionalities in the future. It is maintained by three active universities in variability modelling.

Software product line (SPL) is a software engineering paradigm for software development. A software product within a product line often has specific functionalities that are not common to all other products within the product line. Those specific functionalities are termed “variant features” in a product line. SPL paradigm involves the modeling of variant features. However, little work in SPL investigates and addresses the modeling of variant features specific to user interface (UI). Unified Modeling Language (UML) is the de facto modeling language for object-oriented software systems. It is known that UML needs better support in modeling UIs. Thus, much research developed UML extensions to improve UML support in modeling UIs. Yet little of this work is related to developing such extensions for modeling UIs for SPLs in which variant features specific to UI modeling must be addressed. This research develops a UML extension -Web User Interface Modeling Language (WUIML) to address these problems. WUIML defines elements for modeling variant features specific to user interfaces for Web-based SPLs. The model elements in WUIML extend from the metaclass and BasicActivity of the UML2.0 metamodel. WUIML integrates the modeling of variant features specific to user interfaces to UML. For example, in a Web-based patient registration software product line, member products targeting British users may use British date format in the user interface, while member products targeting United States users may use United States date format in the user interface. Thus, this is a variant feature for this product line. WUIML defines a model element, XOR, to represent such exclusive or conditions in a product line user interface model. WUIML would reduce SPL engineers’ efforts needed in UI development. To validate the WUIML research outcome, a case study was conducted. The results of this empirical study indicate that modeling UIs for Web-based SPLs using WUIML is more effective and efficient than using standard UML.

Software product lines are able to describe multiple products sharing a common base of features and are commonly described as feature models. For complex software product lines, automatic analyses are required to ensure validity and to improve the interactive configuration process. Modern SAT solvers are vital components for the validation process of feature models. The increasing variability of software product lines implies the need to use more expressive solvers like SMT solvers. To assist the development of feature modeling tools, we compare SAT and SMT solvers for the automated analysis of feature models. During this thesis, we create an abstract data type to formally define analyses for feature model defects and their explanations. The result shows that SAT solvers are more efficient at detecting the defects, while SMT solvers can find explanations for them multiple times faster. Feature models can be further expanded by attaching attributes to features. Such attributes may contain a numerical value. Additionally, one attribute can be defined for multiple features. In this thesis, we aim to support the interactive configuration process, by providing the range of the sum of values for an attribute. Such ranges depend on the remaining choices in a configuration of the product line. We provide an exact computation using SMT and an approximation using a heuristic. The evaluation results show that an SMT solver is not suitable for supporting interactive configuration. However, the approximated ranges of the provided heuristic were very close to the exact ones.