Performance testing with the aim of generating an efficient and effective workload to identify performance issues is challenging. Many of the automated approaches mainly rely on analyzing system models, source code, or extracting the usage pattern of the system during the execution. However, such information and artifacts are not always available. Moreover, all the transactions within a generated workload do not impact the performance of the system the same way, a finely tuned workload could accomplish the test objective in an efficient way. Model-free reinforcement learning is widely used for finding the optimal behavior to accomplish an objective in many decision-making problems without relying on a model of the system. This paper proposes that if the optimal policy (way) for generating test workload to meet a test objective can be learned by a test agent, then efficient test automation would be possible without relying on system models or source code. We present a self-adaptive reinforcement learning-driven load testing agent, RELOAD, that learns the optimal policy for test workload generation and generates an effective workload efficiently to meet the test objective. Once the agent learns the optimal policy, it can reuse the learned policy in subsequent testing activities. Our experiments show that the proposed intelligent load test agent can accomplish the test objective with lower test cost compared to common load testing procedures, and results in higher test efficiency.

In testing, engineers want to run the most useful tests early (prioritization). When tests are run hundreds or thousands of times, minimizing a test set can result in significant savings (minimization). This paper proposes a new analysis technique to address both the minimal test set and the test case prioritization problems. This paper precisely defines the concept of mutant stubbornness, which is the basis for our analysis technique. We empirically compare our technique with other test case minimization and prioritization techniques in terms of the size of the minimized test sets and how quickly mutants are killed. We used seven C language subjects from the Siemens Repository, specifically the test sets and the killing matrices from a previous study. We used 30 different orders for each set and ran every technique 100 times over each set. Results show that our analysis technique performed significantly better than prior techniques for creating minimal test sets and was able to establish new bounds for all cases. Also, our analysis technique killed mutants as fast or faster than prior techniques. These results indicate that our mutant stubbornness technique constructs test sets that are both minimal in size, and prioritized effectively, as well or better than other techniques.

The importance of efficient software testing procedures is driven by an ever increasing system complexity as well as global competition. In the particular case of manual test cases at the system integration level, where thousands of test cases may be executed before release, time must be well spent in order to test the system as completely and as efficiently as possible. Automating a subset of the manual test cases, i.e, translating the manual instructions to automatically executable code, is one way of decreasing the test effort. It is further common that test cases exhibit similarities, which can be exploited through reuse when automating a test suite. In this paper, we investigate the potential for reducing test effort by ordering the test cases before such automation, given that we can reuse already automated parts of test cases. In our analysis, we investigate several approaches for prioritization in a case study at a large Swedish vehicular manufacturer. The study analyzes the effects with respect to test effort, on four projects with a total of 3919 integration test cases constituting 35,180 test steps, written in natural language. The results show that for the four projects considered, the difference in expected manual effort between the best and the worst order found is on average 12 percentage points. The results also show that our proposed prioritization method is nearly as good as more resource demanding meta-heuristic approaches at a fraction of the computational time. Based on our results, we conclude that the order of automation is important when the set of test cases contain similar steps (instructions) that cannot be removed, but are possible to reuse. More precisely, the order is important with respect to how quickly the manual test execution effort decreases for a set of test cases that are being automated.

Testing resource allocation is the problem of planning the assignment of resources to testing activities of software components so as to achieve a target goal under given constraints. Existing methods build on software reliability growth models (SRGMs), aiming at maximizing reliability given time/cost constraints, or at minimizing cost given quality/time constraints. We formulate it as a multiobjective debug-aware and robust optimization problem under uncertainty of data, advancing the state-of-the-art in the following ways. Multiobjective optimization produces a set of solutions, allowing to evaluate alternative tradeoffs among reliability, cost, and release time. Debug awareness relaxes the traditional assumptions of SRGMs-in particular the very unrealistic immediate repair of detected faults-and incorporates the bug assignment activity. Robustness provides solutions valid in spite of a degree of uncertainty on input parameters. We show results with a real-world case study.

Growing advances in hardware technologies are enabling significant improvements in application performance by the deployment of components to dedicated executable units. This is particularly valid for Cyber Physical Systems in which the applications are partitioned in HW and SW execution units. The growing complexity of such systems, and increasing requirements, both project- and product-related, makes the partitioning decision process complex. Although different approaches to this decision process have been proposed during recent decades, they lack the ability to provide relevant decisions based on a larger number of requirements and project/business constraints. A sound approach to this problem is taking into account all relevant requirements and constraints and their relations to the properties of the components deployed either as HW or SW units. A typical approach for managing a large number of criteria is a multicriteria decision analysis. This, in its turn, requires uniform definitions of component properties and their realization in respect to their HW/SW deployment. The aim of this paper is twofold: a) to provide an architectural metamodel of component-based applications with specifications of their properties with respect to their partitioning, and b) to categorize component properties in relation to HW/SW deployment. The metamodel enables the transition of system requirements to system and component properties. The categorization provides support for architectural decisions. It is demonstrated through a property guideline for the partitioning of the System Automation and Control domain. The guideline is based on interviews with practitioners and researchers, the experts in this domain.

The analysis of non-functional properties of Service-Based Systems (SBSs) is a complex task, mostly because it requires models that encompass the composition of service properties into architectural properties. For example, the reliability of a SBS is given by the composition of service and interconnection reliabilities. Although several approaches have been introduced in the last few years to address these issues, the tradeoff analysis among non-functional properties of software services has not yet been studied enough. The goal of this paper is to introduce a set of optimization models that allow quantifying the costs of service failure repair/mitigation actions aimed at keeping the whole SBS reliability over a certain threshold. On the basis of our previous work in this area, we first introduce an optimization model aimed at selecting either in-house built or provided services with the goal of minimizing the SBS cost while guaranteeing a certain level of reliability. Thereafter we strengthen the reliability constraints, and we build two different optimization models that aim to solve the same problem under new constraints, where one model starts from the solution obtained in the original model and tries to improve it, while the other one looks for an optimal solution in the whole search space. Finally, we introduce a fourth model, based on stochastic optimization, with the goal of rather searching for solutions that explicitly take into account the stochastic nature of the problem and search for new repair/mitigation actions cheaper than the ones identified by the other models. Each optimization model has been experimented on about 300 variations of a nominal model. The experimental results show the efficacy of our optimization models to quantify the costs of different failure repairing/mitigation actions in different contexts.

In a component-based development process the selection of components is an activity that takes place over multiple lifecycle phases that span from requirement specifications through design to implementation-integration. Automated tool support for component selection would be very helpful in each phase. In this paper we introduce a framework that supports the selection of COTS components in the requirements phase. The framework lays on a tool that builds and solves an optimization model, whose solution provides the optimal COTS component selection. The selection criterion is based on cost minimization of the whole system while assuring a certain degree of satisfaction of the system requirements. The output of the model solution indicates the optimal combination of single COTS components and assemblies of COTS that satisfy the requirements while minimizing costs

The domain of our work is focused on the study of the quality attributes of component-based systems and service-based systems. The qualities of a component-based system depend on those ones of the single components. The objective of our work is to support the selection of (COTS and in-house) components under the satisfactionof the quality attributes. First, we have introduced an optimization model, that supports the component selection activity. The selection criterion is based on cost minimization of the whole system subject to constraints on reliability and delivery time of the whole system. In addition, for each in-house developed component it suggests the amount of testing to perform in order to achieve the required level of reliability. In order to automatize the selection activity we have introduced CODER (Cost Optimization under DElivery and Reliability constraints), a framework that allows to annotate UML diagrams with cost, reliability and delivery time information and that automatically generates and solves the optimization model. In order to improve the formulation of our model we are working on the reliability expression. We express the reliability of the whole system as function of the reliability of the single components by assuming independence among components. We have relaxed this assumption and we have introduced a path-based reliability model for a Service-Oriented Architecture (SOA) that embeds the "errorpropagation" property. Currently, we are working to introduce a framework that automatizes the process of selection of (COTS and in-house) components in all the phases of a component-based development process. In particular, we have introduced a framework thatsupports the selection of COTS components in the requirements phase. The framework lays on a tool that builds and solves an optimization model, whose solution provides the optimal COTS component selection. The selection criterion is based on cost minimization of the whole system while assuring a certain degree of satisfaction of the system requirements.