Inferring test results for dynamic software product lines

In Dynamic Software Product Lines (DSPLs) product configuration occurs at runtime. Modelling variability and configuring the product in DSPLs should consider the context and non-functional requirements (NFRs) satisfaction. ConG4DaS (Contextual Goal models For Dynamic Software product lines) is a Requirements Engineering (RE) approach for DSPL which supports (i) modelling of variability with goals, NFRs, contexts and the relationship between them; and (ii) offers a product configuration based on contexts, NFRs and their priority and interactions. In this paper, we present a survey evaluation of ConG4DaS with RE and DSPL researchers in regard to the perceived usefulness. In the survey, both RE and DSPL researchers perceived ConG4DaS as useful for modelling and configuring DSPLs. However, the RE group gave more positive answers than the his study presents the report about a case study on the application of REACT-M (Requirements Evolution in Agile ContexT - Management), an agile method to support software requirements management, in a real software development environment. This case study assessed the strengths, weaknesses, opportunities, and threats related to the assets that make up REACT-M, namely: work products, life cycle, roles, and ceremonies. About the results, it was observed that REACT-M was effective, simple to use, strongly collaborative, suitable, user centered and goal oriented. Thus, achieved its main purpose to evolve the requirements of a software product in iteratively, efficiently and under of the agile mindset. The results contribute to the software industry by providing empirical data about the use of a agile method to software requirements management, which can serve as a reference for organizations seeking the adoption of agile methods related to the requirements area, as well as to provide the scientific community with a better empirical understanding of the relationship between requirements and agile methodDSPL group.

Emerging domains such as smart homes and more recently smart cities represent a big challenge to software engineering. In such context, the need of runtime self-adaptations to cope with both user needs and environmental changes brings Dynamic Software Product Lines (DSPL) as a suitable solution. However, DSPL implementation itself is challenging, which demands a proper modeling. In this sense, the literature still lacks of means of choosing the modeling technique that best fits a given domain. This paper tackles such problem by defining a criteria for rank such techniques, which is used for ranking a set DSPL modeling techniques found in the literature.

Software product line (SPL) testing consists of two separate but closely related test engineering activities: domain testing and application testing. Various software product line testing approaches have been developed over the last decade, and surveys have been conducted on them. However, thus far none of them deeply addressed the questions of what researches have been conducted in order to overcome the challenges posed by the two separate testing activities and their relationships. Thus, this paper surveys the current software product line testing approaches by defining a reference SPL testing processes and identifying, based on them, key research perspectives that are important in SPL testing. Through this survey, we identify the researches that addressed the challenges and also derive open research opportunities from each perspective.

To tame the complexity of engineering customizable software, systems are built as families of products that share a common core. As customers desire the automatic and smart adjusting of their software to various contexts of operation, runtime reconfiguration capabilities need to be added. In this extended abstract, we sketch reengineering challenges to be tackled when evolving classic software product lines into such dynamic software product lines.

Dynamic Software Product Line (DSPL) Engineering has gained interest through its promise of being able to unify software adaptation whereby software adaptation can be realised at compile time and runtime. While previous work has enabled program logic adaptation by the use of language extensions and platform support, little attention has been placed on Graphical User Interface (GUI) variability. Different design patterns including the Model View Controller are commonly used in GUI implementation, with GUI documents being used for declaring the GUI. To handle dynamic GUI variability currently, the developer needs to implement GUI refinements using multiple techniques. This paper proposes a solution for dealing with GUI document variability, statically and dynamically, in a unified way. In our approach, we currently use a compile time method for producing GUI variants, and code transformations to handle these variants within the application at runtime. To avoid GUI duplicates, only GUI variants that are unique, and related to a valid product configuration, are produced. To validate our approach, we implemented tool support to enable this for Android based applications.

Nowadays, more than one billion people are in need of one or more assistive technologies, and this number is expected to increase beyond two billion by 2050. The majority of assistive technologies are supported by battery-operated devices like smartphones and wearables. This means that battery weight is an important concern in such assistive devices because it may affect negatively its ergonomics. Saving power in these assistive devices is of utmost importance for its potential twofold benefits: extend the device life and reduce the global warming aggravated by billion of these devices. Dynamic Software Product Lines (DSPLs) are a suitable technology that supports system adaptation, in this case, to reduce energy consumption at runtime, considering contextual information and the current state of the device. However, a reduction in battery consumption could negatively affect other quality of service parameters, like response time. Therefore, it is important to trade-off battery saving and these other concerns. This work illustrates how to approach the self-adaptation of smart assistive devices by means of a DSPL-based strategy that optimizes battery consumption taking into account other QoS parameters at the same time. We illustrate our proposal with a real case study: a Smart Cane that is integrated with a DSPL platform, Tanit. Experimentation shows that it is possible to make a trade-off between different quality concerns (energy consumption and relative error). The results of the experiments allow us to conclude that the Tanit approach elongates battery duration of the Smart Cane in one day (an increase of a 6% with a relative error of 1%), so we improve the user quality of experience and reduce the energy footprint with a reasonable relative error.

We envision an Autonomic Software Product Line (ASPL). The ASPL is a dynamic software product line that supports self adaptable products. We plan to use reflective architecture to model and develop ASPL. To evaluate the approach, we have implemented three autonomic product lines which show promising results. The ASPL approach is at initial stages, and require additional work. We plan to exploit online learning to realize more dynamic software product lines to cope with the problem of product line evolution. We propose on-line knowledge sharing among products in a product line to achieve continuous improvement of quality in product line products.

NASA is developing plans for innovative and novel approaches to future (unmanned) space exploration missions. Future missions involve sending spacecraft and robots to harsh environments, where resilience is necessary for the survival of the mission. In addition, distances and communication lead times between the spacecraft and Earth necessitate much of the mission operation being autonomous. We have been conducting research on the development of autonomous space exploration missions based on principles from Autonomic Computing (AC), whereby the mission is imbued with self-management capabilities. Such missions will involve several, rather than single, spacecraft, robots or other devices, operating in collaboration. We describe one such concept mission, ANTS (Autonomous Nano-Technology Swarm), which involves a number of sub-missions that are self-similar. Our work in this, and other future missions, has involved the use of techniques from AC for building in self-management, and ultimately self-governance. We have also explored the use of formal methods to gain confidence in the correct behavior of the mission. Since both the physical devices which will be used for exploration, and the software that is essential for their successful deployment, lend themselves to a product-line approach, we have been exploiting techniques from software product-line engineering, in particular Multi-Agent System Product Lines (MAS-PL) and Dynamic Software Product Lines (DSPL).

Test scenario generation for feature-based context-oriented software systems

Variability management is crucial for companies that need to offer multiple product variants quickly. However, the increasing complexity of software-intensive systems has made variability management increasingly challenging. This challenge is compounded by the need for such systems to run continuously and adapt to changes in the environment and user needs at runtime. To address this challenge, Dynamic Software Product Line (DSPL) Engineering has emerged as a strategy for managing variability in complex and dynamic environments. The key challenge in DSPL engineering is to manage product configurations at runtime by detecting changes in the context and adapting accordingly. In this paper, we propose an adaptation mechanism for DSPL feature models that supports dynamic variability and is based on the MAPE-K model. The mechanism transforms feature model constraints into rules that enable the activation of each feature and annotates contexts in the corresponding features to be activated when changes occur. We have implemented the mechanism in the DyMMer 2.0 modeling tool and evaluated its performance using various DSPL feature models. Additionally, we performed a preliminary evaluation with a proof-of-concept study with an expert to assess its practical usage. Our results demonstrate the effectiveness and practicality of the proposed mechanism in managing variability in complex and dynamic environments.

Service robot has its challenge to adapt on new tasks based on instruction or environment. Such conditions need to reconfigure or reorganize to obtain new system capabilities. However, most common service robot, especially the one that gives services to help human to deliver food or non medical stuffs in an elderly house, suffers on its task flexibility. Unlike a human, robot system needs to be reconfigured to have a new task which may interrupt its services because of this event. To this, we need a technique to handle adaptive tasks that is flexible to change the robot tasks that do not create delay. Dynamic software product line provides a mechanism to reconfigure a system to have different capabilities based on the core assets of the system, such as software that supports and controls the hardware components in a robotic system. In this work, we make use of the dynamic software product line to create a flexible method that enables a service robot to adapt on its new tasks. In order to define the change of tasks, we model the transformation of task using a transition state which has constraints as a trigger. Other than that, the request of change may also have resulted by forcing an interrupt mechanism. To demonstrate the capability of our approach, the proof of concept using Robotic Operating System will be presented.

Today's adaptive software systems (i) are often highly configurable product lines, exhibiting hundreds of potentially conflicting configuration options; (ii) are context dependent, forcing the system to reconfigure to ever-changing contextual situations at runtime; (iii) need to fulfill context-dependent performance goals by optimizing measurable nonfunctional properties. Usually, a large number of consistent configurations exists for a given context, and each consistent configuration may perform differently with regard to the current context and performance goal(s). Therefore, it is crucial to consider nonfunctional properties for identifying an appropriate configuration. Existing black-box approaches for estimating the performance of configurations provide no means for determining context-sensitive reconfiguration decisions at runtime that are both consistent and optimal, and hardly allow for combining multiple context-dependent quality goals. In this paper, we propose a comprehensive approach based on Dynamic Software Product Lines (DSPL) for obtaining consistent and optimal reconfiguration decisions. We use training data obtained from simulations to learn performance-influence models. A novel integrated runtime representation captures both consistency properties and the learned performance-influence models. Our solution provides the flexibility to define multiple context-dependent performance goals. We have implemented our approach as a standalone component. Based on an Internet-of-Things case study using adaptive wireless sensor networks, we evaluate our approach with regard to effectiveness, efficiency, and applicability.

Extensive work on Search-Based Software Testing for Software Product Lines has been published in the last few years. Salient among them is the use of similarity as a surrogate metric for t-wise coverage whenever higher strengths are needed or whenever the size of the test suites is infeasible because of technological or budget limitations. Though promising, this metric has not been assessed with real fault data. In this paper, we address this limitation by using Drupal, a widely used open source web content management system, as an industry-strength case study for which both variability information and fault data have been recently made available. Our preliminary assessment corroborates some of the previous findings but also raises issues on some assumptions and claims made. We hope our work encourages further empirical evaluations of Combinatorial Interaction Testing approaches for Software Product Lines.

Cloud-based technologies play an increasing role in software engineering because of their scalability, availability, and cost efficiency. However, due to privacy issues, developers and organizations still hesitate to host applications that handle sensitive data on servers of external cloud providers. Modern hardware extensions, such as Intel's Software Guard Extensions (SGX), are an attempt to provide confidentiality and integrity for applications running on external hardware. Still, enabling SGX in cloud systems poses new challenges considering scalability and flexibility. In this paper, we propose an approach to address these issues by employing concepts from the domain of Dynamic Software Product Lines (DSPLs). We aim to enable applications running on SGX-based cloud systems to be securely reconfigurable and extendable during runtime. In particular, we describe properties that such an approach should fulfill and discuss corresponding challenges.

In Model Based Testing (MBT), test cases are generated automatically from a partial representation of expected behaviour of the System Under Test (SUT) (i.e., the model). For most industrial systems, it is impossible to generate all the possible test cases from the model. The test engineer recourse to generation algorithms that maximize a given coverage criterion, a metric indicating the percentage of possible behaviours of the SUT covered by the test cases. Our previous work redefined classical Transition Systems (TSs) criteria for SPLs, using Featured Transition Systems (FTSs), a mathematical structure to compactly represent the behaviour of a SPL, as model for test case generation. In this paper, we provide one all-states coverage driven generation algorithm and discuss its scalability and efficiency with respect to random generation. All-states and random generation are compared on fault-seeded FTSs.