In unit testing, a program is decomposed into units which are collections of functions. A part of unit can be tested by generating inputs for a single entry function. The entry function may contain pointer arguments, in which case the inputs to the unit are memory graphs. The paper addresses the problem of automating unit testing with memory graphs as inputs. The approach used builds on previous work combining symbolic and concrete execution, and more specifically, using such a combination to generate test inputs to explore all feasible execution paths. The current work develops a method to represent and track constraints that capture the behavior of a symbolic execution of a unit with memory graphs as inputs. Moreover, an efficient constraint solver is proposed to facilitate incremental generation of such test inputs. Finally, CUTE, a tool implementing the method is described together with the results of applying CUTE to real-world examples of C code.

Regression testing is a crucial part of software development. It checks that software changes do not break existing functionality. An important assumption of regression testing is that test outcomes are deterministic: an unmodified test is expected to either always pass or always fail for the same code under test. Unfortunately, in practice, some testsoften called flaky tests-have non-deterministic outcomes. Such tests undermine the regression testing as they make it difficult to rely on test results.We present the first extensive study of flaky tests. We study in detail a total of 201 commits that likely fix flaky tests in 51 open-source projects. We classify the most common root causes of flaky tests, identify approaches that could manifest flaky behavior, and describe common strategies that developers use to fix flaky tests. We believe that our insights and implications can help guide future research on the important topic of (avoiding) flaky tests.

Continuous integration (CI) systems automate the compilation, building, and testing of software. Despite CI rising as a big success story in automated software engineering, it has received almost no attention from the research community. For example, how widely is CI used in practice, and what are some costs and benefits associated with CI? Without answering such questions, developers, tool builders, and researchers make decisions based on folklore instead of data.In this paper, we use three complementary methods to study the usage of CI in open-source projects. To understand which CI systems developers use, we analyzed 34,544 opensource projects from GitHub. To understand how developers use CI, we analyzed 1,529,291 builds from the most commonly used CI system. To understand why projects use or do not use CI, we surveyed 442 developers. With this data, we answered several key questions related to the usage, costs, and benefits of CI. Among our results, we show evidence that supports the claim that CI helps projects release more often, that CI is widely adopted by the most popular projects, as well as finding that the overall percentage of projects using CI continues to grow, making it important and timely to focus more research on CI. CCS Concepts•Software and its engineering → Agile software development; Software testing and debugging;Keywords continuous integration; mining software repositories ASE'16, September 3-7, 2016, Singapore, Singapore c 2016 ACM. 978-1-4503-3845-5/16/09...$15.00 http://dx.

Abstract.One of the costs of reusing software components is updating applications to use the new version of the components. Updating an application can be error-prone, tedious, and disruptive of the development process. Our previous study showed that more than 80% of the disruptive changes in five different components were caused by refactorings. If the refactorings that happened between two versions of a component could be automatically detected, a refactoring tool could replay them on applications. We present an algorithm that detects refactorings performed during component evolution. Our algorithm uses a combination of a fast syntactic analysis to detect refactoring candidates and a more expensive semantic analysis to refine the results. The experiments on components ranging from 17 KLOC to 352 KLOC show that our algorithm detects refactorings in real-world components with accuracy over 85%.

Regression testing is important but can be time-intensive. One approach to speed it up is regression test selection (RTS), which runs only a subset of tests. RTS was proposed over three decades ago but has not been widely adopted in practice. Meanwhile, testing frameworks, such as JUnit, are widely adopted and well integrated with many popular build systems. Hence, integrating RTS in a testing framework already used by many projects would increase the likelihood that RTS is adopted.We propose a new, lightweight RTS technique, called Ekstazi, that can integrate well with testing frameworks. Ekstazi tracks dynamic dependencies of tests on files, and unlike most prior RTS techniques, Ekstazi requires no integration with version-control systems. We implemented Ekstazi for Java and JUnit, and evaluated it on 615 revisions of 32 open-source projects (totaling almost 5M LOC) with shorter-and longer-running test suites. The results show that Ekstazi reduced the end-to-end testing time 32% on average, and 54% for longer-running test suites, compared to executing all tests. Ekstazi also has lower end-to-end time than the existing techniques, despite the fact that it selects more tests. Ekstazi has been adopted by several popular open source projects, including Apache Camel, Apache Commons Math, and Apache CXF.

We present TestEra, a novel framework for automated testing of Java programs. TestEra automatically generates all non-isomorphic test cases, within a given input size, and evaluates correctness criteria. As an enabling technology, EstEra uses Alloy, a first-order relational language, and the Alloy Analyzer. Checking a program with TestEra involves modeling the correctness criteria f o r the program in Alloy and specifying abstraction and concretization translations between instances of Alloy models and Java data structures. TestEra produces concrete Java inputs as counterexamples to violated correctness criteria. This paper discusses TestEra 's analyses of several case studies: methods that manipulate singly linked lists and red-black trees, a naming architecture, and a part of the Alloy Analyzer.

Abstract. Object-oriented unit tests consist of sequences of method invocations. Behavior of an invocation depends on the method's arguments and the state of the receiver at the beginning of the invocation. Correspondingly, generating unit tests involves two tasks: generating method sequences that build relevant receiverobject states and generating relevant method arguments. This paper proposes Symstra, a framework that achieves both test generation tasks using symbolic execution of method sequences with symbolic arguments. The paper defines symbolic states of object-oriented programs and novel comparisons of states. Given a set of methods from the class under test and a bound on the length of sequences, Symstra systematically explores the object-state space of the class and prunes this exploration based on the state comparisons. Experimental results show that Symstra generates unit tests that achieve higher branch coverage faster than the existing test-generation techniques based on concrete method arguments.

Refactorings are behavior-preserving program transformations that improve the design of a program. Refactoring engines are tools that automate the application of refactorings: first the user chooses a refactoring to apply, then the engine checks if the change is safe, and if so, transforms the program. Refactoring engines are a key component of modern IDEs, and programmers rely on them to perform refactorings. A fault in the refactoring engine can have severe consequences as it can erroneously change large bodies of source code and lead to strenuous debugging sessions.We present a technique for automated testing of refactoring engines. Test inputs for refactoring engines are programs. The core of our technique is a framework for iterative generation of structurally complex test inputs. We instantiate the framework to generate abstract syntax trees that represent Java programs. We also create several kinds of oracles to automatically check that the refactoring engine transformed the generated program correctly. We have applied our technique to testing Eclipse and NetBeans, two popular open-source IDEs for Java, and we have exposed new faults in both: 9 faults in Eclipse and 10 faults in NetBeans.