MORPH: A Reference Architecture for Configuration and Behaviour Self-Adaptation

An architectural approach to self-adaptive systems involves runtime change of system configuration (i.e., the system's components, their bindings and operational parameters) and behaviour update (i.e., component orchestration). Thus, dynamic reconfiguration and discrete event control theory are at the heart of architectural adaptation. Although controlling configuration and behaviour at runtime has been discussed and applied to architectural adaptation, architectures for self-adaptive systems often compound these two aspects reducing the potential for adaptability. In this paper we propose a reference architecture that allows for coordinated yet transparent and independent adaptation of system configuration and behaviour

Towards the Use of the Readily Available Tests from the Release Pipeline as Performance Tests. Are We There Yet?

Performance is one of the important aspects of software quality. In fact, performance issues exist widely in software systems, and the process of fixing the performance issues is an essential step in the release cycle of software systems. Although performance testing is widely adopted in practice, it is still expensive and time-consuming. In particular, the performance testing is usually conducted after the system is built in a dedicated testing environment. The challenge of performance testing makes it difficult to fit into the common DevOps process in software development. On the other hand, there exists a large number of tests readily available, that are executed regularly within the release pipeline during software development. In this paper, we perform an exploratory study to determine whether such readily available tests are capable of serving as performance tests. In particular, we would like to see whether the performance of these tests can demonstrate the performance improvements obtained from fixing real-life performance issues. We collect 127 performance issues from Hadoop and Cassandra and evaluate the performance of the readily available tests from the commits before and after the performance issue fixes. We find that most of the improvements from the fixes to performance issues can be demonstrated using the readily available tests in the release pipeline. However, only a very small portion of the tests can be used for demonstrating the improvements. By manually examining the tests, we identify eight reasons that a test cannot demonstrate performance improvement even though it covers the changed source code of the issue fix. Finally, we build random classifiers determining the important metrics influencing the readily available tests (not) being able to demonstrate performance improvements from issue fixes. We find that the test code itself and the source code covered by the test are important factors, while the factors related to the code changes in the performance issues fixes have low importance. Practitioners should focus on designing and improving the tests, instead of fine-tuning tests for different performance issues fixes. Our findings can be used as a guideline for practitioners to reduce the amount of effort spent on leveraging and designing tests that run in the release pipeline for performance assurance activities

Feature-Model-Guided Online Learning for Self-Adaptive Systems

A self-adaptive system can modify its own structure and behavior at runtime based on its perception of the environment, of itself and of its requirements. To develop a self-adaptive system, software developers codify knowledge about the system and its environment, as well as how adaptation actions impact on the system. However, the codified knowledge may be insufficient due to design time uncertainty, and thus a self-adaptive system may execute adaptation actions that do not have the desired effect. Online learning is an emerging approach to address design time uncertainty by employing machine learning at runtime. Online learning accumulates knowledge at runtime by, for instance, exploring not-yet executed adaptation actions. We address two specific problems with respect to online learning for self-adaptive systems. First, the number of possible adaptation actions can be very large. Existing online learning techniques randomly explore the possible adaptation actions, but this can lead to slow convergence of the learning process. Second, the possible adaptation actions can change as a result of system evolution. Existing online learning techniques are unaware of these changes and thus do not explore new adaptation actions, but explore adaptation actions that are no longer valid. We propose using feature models to give structure to the set of adaptation actions and thereby guide the exploration process during online learning. Experimental results involving four real-world systems suggest that considering the hierarchical structure of feature models may speed up convergence by 7.2% on average. Considering the differences between feature models before and after an evolution step may speed up convergence by 64.6% on average. [...

Opinion Mining for Software Development: A Systematic Literature Review

Opinion mining, sometimes referred to as sentiment analysis, has gained increasing attention in software engineering (SE) studies. SE researchers have applied opinion mining techniques in various contexts, such as identifying developers’ emotions expressed in code comments and extracting users’ critics toward mobile apps. Given the large amount of relevant studies available, it can take considerable time for researchers and developers to figure out which approaches they can adopt in their own studies and what perils these approaches entail. We conducted a systematic literature review involving 185 papers. More specifically, we present 1) well-defined categories of opinion mining-related software development activities, 2) available opinion mining approaches, whether they are evaluated when adopted in other studies, and how their performance is compared, 3) available datasets for performance evaluation and tool customization, and 4) concerns or limitations SE researchers might need to take into account when applying/customizing these opinion mining techniques. The results of our study serve as references to choose suitable opinion mining tools for software development activities, and provide critical insights for the further development of opinion mining techniques in the SE domain

An Analysis of Patch Plausibility and Correctness for Generate-And-Validate Patch Generation Systems

We analyze reported patches for three existing generate-and-validate patch generation systems (GenProg, RSRepair, and AE). The basic principle behind generate-and-validate systems is to accept only plausible patches that produce correct outputs for all inputs in the test suite used to validate the patches. Because of errors in the patch evaluation infrastructure, the majority of the reported patches are not plausible --- they do not produce correct outputs even for the inputs in the validation test suite. The overwhelming majority of the reported patches are not correct and are equivalent to a single modification that simply deletes functionality. Observed negative effects include the introduction of security vulnerabilities and the elimination of desirable standard functionality. We also present Kali, a generate-and-validate patch generation system that only deletes functionality. Working with a simpler and more effectively focused search space, Kali generates at least as many correct patches as prior GenProg, RSRepair, and AE systems. Kali also generates at least as many patches that produce correct outputs for the inputs in the validation test suite as the three prior systems. We also discuss patches produced by ClearView, a generate-and-validate binary hot patching system that leverages learned invariants to produce patches that enable systems to survive otherwise fatal defects and security attacks. Our analysis indicates that ClearView successfully patches 9 of the 10 security vulnerabilities used to evaluate the system. At least 4 of these patches are correct