Razieh Behjati

SimPL: A product-line modeling methodology for families of integrated control systems

Context: Integrated control systems (ICSs) are heterogeneous systems where software and hardware components are integrated to control and monitor physical devices and processes. A family of ICSs share the same software code base, which is configured differently for each product to form a unique installation. Due to the complexity of ICSs and inadequate automation support, product configuration in this context is typically error-prone and costly. Objective: As a first step to overcome these challenges, we propose a UML-based product-line modeling methodology that provides a foundation for semi-automated product configuration in the specific context of ICSs. Method: We performed a comprehensive domain analysis to identify characteristics of ICS families, and their configuration challenges. Based on this, we formulated the characteristics of an adequate configuration solution, and derived from them a set of modeling requirements for a model-based solution to configuration. The SimPL methodology is proposed to fulfill these requirements. Results: To evaluate the ability of SimPL to fulfill the modeling requirements, we applied it to a large-scale industrial case study. Our experience with the case study shows that SimPL is adequate to provide a model of the product family that meets the modeling requirements. Further evaluation is still required to assess the applicability and scalability of SimPL in practice. Doing this requires conducting field studies with human subjects and is left for future work. Conclusion: We conclude that configuration in ICSs requires better automation support, and UML-based approaches to product family modeling can be tailored to provide the required foundation.

Extending sysML with AADL concepts for comprehensive system architecture modeling

Recent years have seen a proliferation of languages for describing embedded systems. Some of these languages have emerged from domain-specific frameworks, and some are adaptions or extensions of more general-purpose languages. In this paper, we focus on two widelyused standard languages: the Architecture Analysis and Design Language (AADL) and the Systems Modeling Language (SysML). AADL was born as an avionics-focused domain-specific language and later on has been revised to represent and support a more general category of embedded realtime systems. SysML is an extension of the Unified Modeling Language (UML) intended to support system engineering and modeling. We propose the ExSAM profile that extends SysML by adding AADL concepts to it with the goal of exploiting the key advantages of both languages in a seamless way. More precisely, by using ExSAM and any SysML modeling environment, we will be able to both model system engineering concepts and use AADL analysis tools where needed. We describe the ExSAM profile through several examples and compare it with existing alternatives. We have implemented ExSAM using IBM Rational Rhapsody and evaluated its completeness and usefulness through two case studies.

Synthesis of attributed feature models from product descriptions

Many real-world product lines are only represented as nonhierarchical collections of distinct products, described by their configuration values. As the manual preparation of feature models is a tedious and labour-intensive activity, some techniques have been proposed to automatically generate boolean feature models from product descriptions. However, none of these techniques is capable of synthesizing feature attributes and relations among attributes, despite the huge relevance of attributes for documenting software product lines. In this paper, we introduce for the first time an algorithmic and parametrizable approach for computing a legal and appropriate hierarchy of features, including feature groups, typed feature attributes, domain values and relations among these attributes. We have performed an empirical evaluation by using both randomized configuration matrices and real-world examples. The initial results of our evaluation show that our approach can scale up to matrices containing 2,000 attributed features, and 200,000 distinct configurations in a couple of minutes.

Improving change recommendation using aggregated association rules

Past research has proposed association rule mining as a means to uncover the evolutionary coupling from a system’s change history. These couplings have various applications, such as improving system decomposition and recommending related changes during development. The strength of the coupling can be characterized using a variety of interestingness measures. Existing recommendation engines typically use only the rule with the highest interestingness value in situations where more than one rule applies. In contrast, we argue that multiple applicable rules indicate increased evidence, and hypothesize that the aggregation of such rules can be exploited to provide more accurate recommendations.To investigate this hypothesis we conduct an empirical study on the change histories of two large industrial systems and four large open source systems. As aggregators we adopt three cumulative gain functions from information retrieval. The experiments evaluate the three using 39 different rule interestingness measures. The results show that aggregation provides a significant impact on most measure’s value and, furthermore, leads to a significant improvement in the resulting recommendation.

Generalizing the Analysis of Evolutionary Coupling for Software Change Impact Analysis

Software change impact analysis aims to find artifacts potentially affected by a change. Typical approaches apply language-specific static or dynamic dependence analysis, and are thus restricted to homogeneous systems. This restriction is a major drawback given todays increasingly heterogeneous software. Evolutionary coupling has been proposed as a language-agnostic alternative that mines relations between source-code entities from the systems change history. Unfortunately, existing evolutionary coupling based techniques fall short. For example, using Singular Value Decomposition (SVD) quickly becomes computationally expensive. An efficient alternative applies targeted association rule mining, but the most widely known approach (ROSE) has restricted applicability: experiments on two large industrial systems, and four large open source systems, show that ROSE can only identify dependencies about 25% of the time. To overcome this limitation, we introduce TARMAQ, a new algorithm for mining evolutionary coupling. Empirically evaluated on the same six systems, TARMAQ performs consistently better than ROSE and SVD, is applicable 100% of the time, and runs orders of magnitude faster than SVD. We conclude that the proposed algorithm is a significant step forward towards achieving robust change impact analysis for heterogeneous systems.

Architecture-Level Configuration of Large-Scale Embedded Software Systems

Configuration in the domain of Integrated Control Systems (ICS) is largely manual, laborious, and error prone. In this article, we propose a model-based configuration approach that provides automation support for reducing configuration effort and the likelihood of configuration errors in the ICS domain. We ground our approach on component-based specifications of ICS families. We then develop a configuration algorithm using constraint satisfaction techniques over finite domains to generate products that are consistent with respect to their ICS family specifications. We reason about the termination and consistency of our configuration algorithm analytically. We evaluate the effectiveness of our configuration approach by applying it to a real subsea oil production system. Specifically, we have rebuilt a number of existing verified product configurations of our industry partner. Our experience shows that our approach can automatically infer up to 50% of the configuration decisions, and reduces the complexity of making configuration decisions.

Towards a systematic requirement-based test generation framework: Industrial challenges and needs

Requirement-based test generation (RBTG) is a verification and validation technique, which ensures the conformance of a final product with its requirements. In collaboration with an industry partner, we studied and analyzed their current practice of applying RBTG in the context of developing a family of subsea oil and gas production systems, which are cyber-physical systems. The company aims at improving their current RBTG practice by enhancing the reuse of test artifacts across different products. Due to the complexity of developing such systems and being in the context of system product-line engineering, achieving this goal requires a systematic approach for RBTG. As the first step to this end, we conducted a domain analysis with the industry partner to characterize their current practice of applying RBTG and to identify their needs and challenges. In this paper, we report results of the domain analysis. Moreover, we discuss the limitations of employing existing RBTG approaches in an industrial setting and suggest directions for improvement.

A modeling approach to support the similarity-based reuse of configuration data

Product configuration in families of Integrated Control Systems (ICSs) involves resolving thousands of configurable parameters and is, therefore, time-consuming and error-prone. Typically, these systems consist of highly similar components that need to be configured similarly. For large-scale systems, a considerable portion of the configuration data can be reused, based on such similarities, during the configuration of each individual product. In this paper, we propose a model-based approach to automate the reuse of configuration data based on the similarities within an ICS product. Our approach enables configuration engineers to manipulate the reuse of configuration data, and ensures the consistency of the reused data. Evaluation of the approach, using a number of configured products from an industry partner, shows that more than 60% of configuration data can be automatically reused using our similarity-based approach, thereby reducing configuration effort.

Aggregating Association Rules to Improve Change Recommendation

As the complexity of software systems grows, it becomes increasingly difficult for developers to be aware of all the dependencies that exist between artifacts (e.g., files or methods) of a system. Change recommendation has been proposed as a technique to overcome this problem, as it suggests to a developer relevant source-code artifacts related to her changes. Association rule mining has shown promise in deriving such recommendations by uncovering relevant patterns in the system’s change history. The strength of the mined association rules is captured using a variety of interestingness measures. However, state-of-the-art recommendation engines typically use only the rule with the highest interestingness value when more than one rule applies. In contrast, we argue that when multiple rules apply, this indicates collective evidence, and aggregating those rules (and their evidence) will lead to more accurate change recommendation. To investigate this hypothesis we conduct a large empirical study of 15 open source software systems and two systems from our industry partners. We evaluate association rule aggregation using four variants of the change history for each system studied, enabling us to compare two different levels of granularity in two different scenarios. Furthermore, we study 40 interestingness measures using the rules produced by two different mining algorithms. The results show that (1) between 13 and 90% of change recommendations can be improved by rule aggregation, (2) rule aggregation almost always improves change recommendation for both algorithms and all measures, and (3) fine-grained histories benefit more from rule aggregation.

Efficient Architecture-Level Configuration of Large-Scale Embedded Software Systems

Configuration is a recurring problem in many domains. In our earlier work, we focused on architecture-level configuration of largescale embedded software systems and proposed a methodology that enables engineers to configure products by instantiating a given reference architecture model. Products have to satisfy a number of constraints specified in the reference architecture model. If not, the engineers have to backtrack their configuration decisions to rebuild a configured product that satisfies the constraints. Backtracking configuration decisions makes the configuration process considerably slow. In this paper, we improve our earlier work and propose a backtrack-free configuration mechanism. Specifically, given a cycle-free generic reference architecture model, we propose an algorithm that computes an ordering over configuration parameters that yields a consistent configuration without any need to backtrack. We evaluated our approach on a simplified model of an industrial case study.We show that our ordering approach eliminates backtracking. It reduces the overall configuration time by both reducing the required number of value assignments, and reducing the time that it takes to complete one configuration iteration. Furthermore, we show that the latter has a linear growth with the size of the configuration problem.