Inferring Concise Specifications of APIs

Modern software relies on libraries and uses them via application programming interfaces (APIs). Correct API usage as well as many software engineering tasks are enabled when APIs have formal specifications. In this work, we analyze the implementation of each method in an API to infer a formal postcondition. Conventional wisdom is that, if one has preconditions, then one can use the strongest postcondition predicate transformer (SP) to infer postconditions. However, SP yields postconditions that are exponentially large, which makes them difficult to use, either by humans or by tools. Our key idea is an algorithm that converts such exponentially large specifications into a form that is more concise and thus more usable. This is done by leveraging the structure of the specifications that result from the use of SP. We applied our technique to infer postconditions for over 2,300 methods in seven popular Java libraries. Our technique was able to infer specifications for 75.7% of these methods, each of which was verified using an Extended Static Checker. We also found that 84.6% of resulting specifications were less than 1/4 page (20 lines) in length. Our technique was able to reduce the length of SMT proofs needed for verifying implementations by 76.7% and reduced prover execution time by 26.7%

Feedback-Driven Dynamic Invariant Discovery

Program invariants can help software developers identify program properties that must be preserved as the software evolves, however, formulating correct invariants can be challenging. In this work, we introduce iDiscovery, a technique which leverages symbolic execution to improve the quality of dynamically discovered invariants computed by Daikon. Candidate invariants generated by Daikon are synthesized into assertions and instrumented onto the program. The instrumented code is executed symbolically to generate new test cases that are fed back to Daikon to help further re ne the set of candidate invariants. This feedback loop is executed until a x-point is reached. To mitigate the cost of symbolic execution, we present optimizations to prune the symbolic state space and to reduce the complexity of the generated path conditions. We also leverage recent advances in constraint solution reuse techniques to avoid computing results for the same constraints across iterations. Experimental results show that iDiscovery converges to a set of higher quality invariants compared to the initial set of candidate invariants in a small number of iterations

High System-Code Security with Low Overhead

Security vulnerabilities plague modern systems because writing secure systems code is hard. Promising approaches can retrofit security automatically via runtime checks that implement the desired security policy; these checks guard critical operations, like memory accesses. Alas, the induced slowdown usually exceeds by a wide margin what system users are willing to tolerate in production, so these tools are hardly ever used. As a result, the insecurity of real-world systems persists. We present an approach in which developers/operators can specify what level of overhead they find acceptable for a given workload (e.g., 5%); our proposed tool ASAP then automatically instruments the program to maximize its security while staying within the specified "overhead budget." Two insights make this approach effective: most overhead in existing tools is due to only a few "hot" checks, whereas the checks most useful to security are typically "cold" and cheap. We evaluate ASAP on programs from the Phoronix and SPEC benchmark suites. It can precisely select the best points in the security-performance spectrum. Moreover, we analyzed existing bugs and security vulnerabilities in RIPE, OpenSSL, and the Python interpreter, and found that the protection level offered by the ASAP approach is sufficient to protect against all of them

Improving Android App Responsiveness through Search-Based Frame Rate Reduction

Responsiveness is one of the most important properties of Android applications to both developers and users. Recent survey on automated improvement of non-functional properties of Android applications shows there is a gap in the application of search-based techniques to improve responsiveness. Therefore, we explore the use of genetic improvement (GI) to achieve this task. We extend Gin, an open source GI framework, to work with Android applications. Next, we apply GI to four open source Android applications, measuring frame rate as proxy for responsiveness. We find that while there are improvements to be found in UI-implementing code (up to 43%), often applications’ test suites are not strong enough to safely perform GI, leading to generation of many invalid patches. We also apply GI to areas of code which have highest test-suite coverage, but find no patches leading to consistent frame rate reductions. This shows that although GI could be successful in improvement of Android apps’ responsiveness, any such test-based technique is currently hindered by availability of test suites covering UI elements

Praspel: Contract-Driven Testing for PHP using Realistic Domains

We present an integrated contract-based testing framework for PHP. It relies on a behavioral interface specification language called Praspel, for "PHP Realistic Annotation and Specification Language". Using Praspel developers can easily annotate their PHP scripts with formal contracts, namely class invariants, and method pre- and postconditions. These contracts describe assertions either by predicates or by assigning realistic domains to data. Realistic domains introduce types in PHP and describe complex structures frequently encountered in applications, such as email addresses or SQL queries. Realistic domains display two properties: predicability, which allows to check if a data belongs to a given realistic domain, and samplability, which allows to generate valid data. This paper introduces coverage criteria dedicated to contracts, designed to exhibit relevant behaviors of the annotated methods. Test data are then computed to satisfy these coverage criteria, by using dedicated data generators for complex realistic domains, such as arrays or strings. This framework has been implemented and disseminated within the PHP community, which gave us feedback on their usage of the tool and the relevance of this integrated process with respect to their practice of manual testing

Reverse Engineering and Testing of Rich Internet Applications

The World Wide Web experiences a continuous and constant evolution, where new initiatives, standards, approaches and technologies are continuously proposed for developing more effective and higher quality Web applications. To satisfy the growing request of the market for Web applications, new technologies, frameworks, tools and environments that allow to develop Web and mobile applications with the least effort and in very short time have been introduced in the last years. These new technologies have made possible the dawn of a new generation of Web applications, named Rich Internet Applications (RIAs), that offer greater usability and interactivity than traditional ones. This evolution has been accompanied by some drawbacks that are mostly due to the lack of applying well-known software engineering practices and approaches. As a consequence, new research questions and challenges have emerged in the field of web and mobile applications maintenance and testing. The research activity described in this thesis has addressed some of these topics with the specific aim of proposing new and effective solutions to the problems of modelling, reverse engineering, comprehending, re-documenting and testing existing RIAs. Due to the growing relevance of mobile applications in the renewed Web scenarios, the problem of testing mobile applications developed for the Android operating system has been addressed too, in an attempt of exploring and proposing new techniques of testing automation for these type of applications

Dynamic code coverage with progressive detail levels

Tese de Mestrado Integrado. Engenharia Informática e Computação. Faculdade de Engenharia. Universidade do Porto. 201