Abstract Traditionally, software development teams in many industries have used copies of production databases or their masked, anonymized, or obfuscated versions for testing. However, privacy protection regulations, for example, the General Data Protection Regulation (GDPR), prohibit such practices. In such a situation, there is often a need to generate production-like test data, i.e., test data that is statistically representative of the production data and conforms to the domain’s constraints. In this paper, we address this need by presenting a novel approach for generating production-like test data using deep learning techniques and studying the practical effectiveness of our proposed approach in industrial settings. We frame the problem of generating production-like test data as a Language Modeling problem. We then propose a general solution for test data generation and a framework for evaluating and comparing language models based on training effectiveness and the representativeness and validity of the generated data. To evaluate the practical effectiveness of our solution, we apply it to a case study: the Norwegian National Population Registry (NPR). Within the context of NPR, we experiment with three of the most successful Deep Learning algorithms for Language Modeling, namely Recurrent Neural Networks (RNN), Variational Autoencoders, and Generative Adversarial Networks (GANs). We furthermore evaluate and compare the effectiveness of these algorithms quantitatively, using the proposed evaluation framework. The results from our case study show that our approach can generate highly complex data that is statistically representative of the production data and conforms to the business rules of the domain. Moreover, test data generated with RNN, which outperforms the other two algorithms, are syntactically and semantically valid in more than 97% of the cases and are highly representative of the real NPR data. The practical applicability of our approach is evident from the fact that our approach was fully deployed in a test environment at the NPR, generating on-the-fly and scalable amounts of production-like test data that are used in the integration testing between NPR and its data consumers.

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.

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 today's increasingly heterogeneous software. Evolutionary coupling has been proposed as a language-agnostic alternative that mines relations between source-code entities from the system's 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.

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.

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.

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.

In an industrial context, software systems are rarely developed by a single organization. For software product lines, this means that various organizations collaborate to provide and integrate the assets used in a product line. It is not uncommon that these assets themselves are built as product lines, a practice which is referred to as multi product lines. This cross-organizational distribution of reusable assets leads to numerous challenges, such as inconsistent configuration, costly and time-consuming integration, diverging evolution speed and direction, and inadequate testing.

SystemC is a system-level modeling language that can be used effectively for hardware/software co-design. Since a major goal of SystemC is to enable verification at higher levels of abstraction, the tendency is now directing to introducing formal verification approaches for SystemC. In this article, we propose an approach for formal verification of SystemC designs, and provide the semantics of SystemC using Labeled Transition Systems (LTS) for this purpose. An actor-based language, Rebeca, is used as an intermediate language. SystemC designs are mapped to Rebeca models and then Rebeca verification toolset is used to verify LTL and CTL properties. To tackle the state-space explosion, Rebeca model checkers offer some reduction policies that make them appropriate for SystemC verification. The approach also benefits from the modular verification and program slicing techniques applied on Rebeca models. To show the applicability of our approach, we verified a single-cycle MIPS design and two hardware/software co-designs. The results show that our approach can effectively be used both in hardware and hardware/software co-verification.