Efficiently Manifesting Asynchronous Programming Errors in Android Apps

Android, the #1 mobile app framework, enforces the single-GUI-thread model, in which a single UI thread manages GUI rendering and event dispatching. Due to this model, it is vital to avoid blocking the UI thread for responsiveness. One common practice is to offload long-running tasks into async threads. To achieve this, Android provides various async programming constructs, and leaves developers themselves to obey the rules implied by the model. However, as our study reveals, more than 25% apps violate these rules and introduce hard-to-detect, fail-stop errors, which we term as aysnc programming errors (APEs). To this end, this paper introduces APEChecker, a technique to automatically and efficiently manifest APEs. The key idea is to characterize APEs as specific fault patterns, and synergistically combine static analysis and dynamic UI exploration to detect and verify such errors. Among the 40 real-world Android apps, APEChecker unveils and processes 61 APEs, of which 51 are confirmed (83.6% hit rate). Specifically, APEChecker detects 3X more APEs than the state-of-art testing tools (Monkey, Sapienz and Stoat), and reduces testing time from half an hour to a few minutes. On a specific type of APEs, APEChecker confirms 5X more errors than the data race detection tool, EventRacer, with very few false alarms

Natural Language is a Programming Language: Applying Natural Language Processing to Software Development

A powerful, but limited, way to view software is as source code alone. Treating a program as a sequence of instructions enables it to be formalized and makes it amenable to mathematical techniques such as abstract interpretation and model checking. A program consists of much more than a sequence of instructions. Developers make use of test cases, documentation, variable names, program structure, the version control repository, and more. I argue that it is time to take the blinders off of software analysis tools: tools should use all these artifacts to deduce more powerful and useful information about the program. Researchers are beginning to make progress towards this vision. This paper gives, as examples, four results that find bugs and generate code by applying natural language processing techniques to software artifacts. The four techniques use as input error messages, variable names, procedure documentation, and user questions. They use four different NLP techniques: document similarity, word semantics, parse trees, and neural networks. The initial results suggest that this is a promising avenue for future work

Understanding Android Obfuscation Techniques: A Large-Scale Investigation in the Wild

In this paper, we seek to better understand Android obfuscation and depict a holistic view of the usage of obfuscation through a large-scale investigation in the wild. In particular, we focus on four popular obfuscation approaches: identifier renaming, string encryption, Java reflection, and packing. To obtain the meaningful statistical results, we designed efficient and lightweight detection models for each obfuscation technique and applied them to our massive APK datasets (collected from Google Play, multiple third-party markets, and malware databases). We have learned several interesting facts from the result. For example, malware authors use string encryption more frequently, and more apps on third-party markets than Google Play are packed. We are also interested in the explanation of each finding. Therefore we carry out in-depth code analysis on some Android apps after sampling. We believe our study will help developers select the most suitable obfuscation approach, and in the meantime help researchers improve code analysis systems in the right direction

Un outil d'assistance à la construction de tests de modèles à composants et services

National audienceDans l’article "COSTOTest : A Tool for Building and Running Test Harness forService-Based Component Models" publié dans les proceedings de la conférenceinternationale ISSTA 2016 [1] et présenté en session démonstration, nous décrivonscomment l’outil COSTOTest nous assiste pour tester directement les modèles decomposants logiciels basés sur les services. Ces travaux concernent la vérificationde systèmes logiciels à base de composants et services (Service-based Componentou SBC) et exploitent l’ingénierie dirigée par les modèles (IDM)

Un outil d'assistance à la construction de tests de modèles à composants et services

National audienceDans l’article "COSTOTest : A Tool for Building and Running Test Harness forService-Based Component Models" publié dans les proceedings de la conférenceinternationale ISSTA 2016 [1] et présenté en session démonstration, nous décrivonscomment l’outil COSTOTest nous assiste pour tester directement les modèles decomposants logiciels basés sur les services. Ces travaux concernent la vérificationde systèmes logiciels à base de composants et services (Service-based Componentou SBC) et exploitent l’ingénierie dirigée par les modèles (IDM)

Large-Scale Analysis of Framework-Specific Exceptions in Android Apps

Mobile apps have become ubiquitous. For app developers, it is a key priority to ensure their apps' correctness and reliability. However, many apps still suffer from occasional to frequent crashes, weakening their competitive edge. Large-scale, deep analyses of the characteristics of real-world app crashes can provide useful insights to guide developers, or help improve testing and analysis tools. However, such studies do not exist -- this paper fills this gap. Over a four-month long effort, we have collected 16,245 unique exception traces from 2,486 open-source Android apps, and observed that framework-specific exceptions account for the majority of these crashes. We then extensively investigated the 8,243 framework-specific exceptions (which took six person-months): (1) identifying their characteristics (e.g., manifestation locations, common fault categories), (2) evaluating their manifestation via state-of-the-art bug detection techniques, and (3) reviewing their fixes. Besides the insights they provide, these findings motivate and enable follow-up research on mobile apps, such as bug detection, fault localization and patch generation. In addition, to demonstrate the utility of our findings, we have optimized Stoat, a dynamic testing tool, and implemented ExLocator, an exception localization tool, for Android apps. Stoat is able to quickly uncover three previously-unknown, confirmed/fixed crashes in Gmail and Google+; ExLocator is capable of precisely locating the root causes of identified exceptions in real-world apps. Our substantial dataset is made publicly available to share with and benefit the community.Comment: ICSE'18: the 40th International Conference on Software Engineerin

Evidence-driven testing and debugging of software systems

Program debugging is the process of testing, exposing, reproducing, diagnosing and fixing software bugs. Many techniques have been proposed to aid developers during software testing and debugging. However, researchers have found that developers hardly use or adopt the proposed techniques in software practice. Evidently, this is because there is a gap between proposed methods and the state of software practice. Most methods fail to address the actual needs of software developers. In this dissertation, we pose the following scientific question: How can we bridge the gap between software practice and the state-of-the-art automated testing and debugging techniques? To address this challenge, we put forward the following thesis: Software testing and debugging should be driven by empirical evidence collected from software practice. In particular, we posit that the feedback from software practice should shape and guide (the automation) of testing and debugging activities. In this thesis, we focus on gathering evidence from software practice by conducting several empirical studies on software testing and debugging activities in the real-world. We then build tools and methods that are well-grounded and driven by the empirical evidence obtained from these experiments. Firstly, we conduct an empirical study on the state of debugging in practice using a survey and a human study. In this study, we ask developers about their debugging needs and observe the tools and strategies employed by developers while testing, diagnosing and repairing real bugs. Secondly, we evaluate the effectiveness of the state-of-the-art automated fault localization (AFL) methods on real bugs and programs. Thirdly, we conducted an experiment to evaluate the causes of invalid inputs in software practice. Lastly, we study how to learn input distributions from real-world sample inputs, using probabilistic grammars. To bridge the gap between software practice and the state of the art in software testing and debugging, we proffer the following empirical results and techniques: (1) We collect evidence on the state of practice in program debugging and indeed, we found that there is a chasm between (available) debugging tools and developer needs. We elicit the actual needs and concerns of developers when testing and diagnosing real faults and provide a benchmark (called DBGBench) to aid the automated evaluation of debugging and repair tools. (2) We provide empirical evidence on the effectiveness of several state-of-the-art AFL techniques (such as statistical debugging formulas and dynamic slicing). Building on the obtained empirical evidence, we provide a hybrid approach that outperforms the state-of-the-art AFL techniques. (3) We evaluate the prevalence and causes of invalid inputs in software practice, and we build on the lessons learned from this experiment to build a general-purpose algorithm (called ddmax) that automatically diagnoses and repairs real-world invalid inputs. (4) We provide a method to learn the distribution of input elements in software practice using probabilistic grammars and we further employ the learned distribution to drive the test generation of inputs that are similar (or dissimilar) to sample inputs found in the wild. In summary, we propose an evidence-driven approach to software testing and debugging, which is based on collecting empirical evidence from software practice to guide and direct software testing and debugging. In our evaluation, we found that our approach is effective in improving the effectiveness of several debugging activities in practice. In particular, using our evidence-driven approach, we elicit the actual debugging needs of developers, improve the effectiveness of several automated fault localization techniques, effectively debug and repair invalid inputs, and generate test inputs that are (dis)similar to real-world inputs. Our proposed methods are built on empirical evidence and they improve over the state-of-the-art techniques in testing and debugging.Software-Debugging bezeichnet das Testen, Aufspüren, Reproduzieren, Diagnostizieren und das Beheben von Fehlern in Programmen. Es wurden bereits viele Debugging-Techniken vorgestellt, die Softwareentwicklern beim Testen und Debuggen unterstützen. Dennoch hat sich in der Forschung gezeigt, dass Entwickler diese Techniken in der Praxis kaum anwenden oder adaptieren. Das könnte daran liegen, dass es einen großen Abstand zwischen den vorgestellten und in der Praxis tatsächlich genutzten Techniken gibt. Die meisten Techniken genügen den Anforderungen der Entwickler nicht. In dieser Dissertation stellen wir die folgende wissenschaftliche Frage: Wie können wir die Kluft zwischen Software-Praxis und den aktuellen wissenschaftlichen Techniken für automatisiertes Testen und Debugging schließen? Um diese Herausforderung anzugehen, stellen wir die folgende These auf: Das Testen und Debuggen von Software sollte von empirischen Daten, die in der Software-Praxis gesammelt wurden, vorangetrieben werden. Genauer gesagt postulieren wir, dass das Feedback aus der Software-Praxis die Automation des Testens und Debuggens formen und bestimmen sollte. In dieser Arbeit fokussieren wir uns auf das Sammeln von Daten aus der Software-Praxis, indem wir einige empirische Studien über das Testen und Debuggen von Software in der echten Welt durchführen. Auf Basis der gesammelten Daten entwickeln wir dann Werkzeuge, die sich auf die Daten der durchgeführten Experimente stützen. Als erstes führen wir eine empirische Studie über den Stand des Debuggens in der Praxis durch, wobei wir eine Umfrage und eine Humanstudie nutzen. In dieser Studie befragen wir Entwickler zu ihren Bedürfnissen, die sie beim Debuggen haben und beobachten die Werkzeuge und Strategien, die sie beim Diagnostizieren, Testen und Aufspüren echter Fehler einsetzen. Als nächstes bewerten wir die Effektivität der aktuellen Automated Fault Localization (AFL)- Methoden zum automatischen Aufspüren von echten Fehlern in echten Programmen. Unser dritter Schritt ist ein Experiment, um die Ursachen von defekten Eingaben in der Software-Praxis zu ermitteln. Zuletzt erforschen wir, wie Häufigkeitsverteilungen von Teileingaben mithilfe einer Grammatik von echten Beispiel-Eingaben aus der Praxis gelernt werden können. Um die Lücke zwischen Software-Praxis und der aktuellen Forschung über Testen und Debuggen von Software zu schließen, bieten wir die folgenden empirischen Ergebnisse und Techniken: (1) Wir sammeln aktuelle Forschungsergebnisse zum Stand des Software-Debuggens und finden in der Tat eine Diskrepanz zwischen (vorhandenen) Debugging-Werkzeugen und dem, was der Entwickler tatsächlich benötigt. Wir sammeln die tatsächlichen Bedürfnisse von Entwicklern beim Testen und Debuggen von Fehlern aus der echten Welt und entwickeln einen Benchmark (DbgBench), um das automatische Evaluieren von Debugging-Werkzeugen zu erleichtern. (2) Wir stellen empirische Daten zur Effektivität einiger aktueller AFL-Techniken vor (z.B. Statistical Debugging-Formeln und Dynamic Slicing). Auf diese Daten aufbauend, stellen wir einen hybriden Algorithmus vor, der die Leistung der aktuellen AFL-Techniken übertrifft. (3) Wir evaluieren die Häufigkeit und Ursachen von ungültigen Eingaben in der Softwarepraxis und stellen einen auf diesen Daten aufbauenden universell einsetzbaren Algorithmus (ddmax) vor, der automatisch defekte Eingaben diagnostiziert und behebt. (4) Wir stellen eine Methode vor, die Verteilung von Schnipseln von Eingaben in der Software-Praxis zu lernen, indem wir Grammatiken mit Wahrscheinlichkeiten nutzen. Die gelernten Verteilungen benutzen wir dann, um den Beispiel-Eingaben ähnliche (oder verschiedene) Eingaben zu erzeugen. Zusammenfassend stellen wir einen auf der Praxis beruhenden Ansatz zum Testen und Debuggen von Software vor, welcher auf empirischen Daten aus der Software-Praxis basiert, um das Testen und Debuggen zu unterstützen. In unserer Evaluierung haben wir festgestellt, dass unser Ansatz effektiv viele Debugging-Disziplinen in der Praxis verbessert. Genauer gesagt finden wir mit unserem Ansatz die genauen Bedürfnisse von Entwicklern, verbessern die Effektivität vieler AFL-Techniken, debuggen und beheben effektiv fehlerhafte Eingaben und generieren Test-Eingaben, die (un)ähnlich zu Eingaben aus der echten Welt sind. Unsere vorgestellten Methoden basieren auf empirischen Daten und verbessern die aktuellen Techniken des Testens und Debuggens

Variationally consistent computational homogenization of chemomechanical problems with stabilized weakly periodic boundary conditions

A variationally consistent model-based computational homogenization approach for transient chemomechanically coupled problems is developed based on the classical assumption of first-order prolongation of the displacement, chemical potential, and (ion) concentration fields within a representative volume element (RVE). The presence of the chemical potential and the concentration as primary global fields represents a mixed formulation, which has definite advantages. Nonstandard diffusion, governed by a Cahn–Hilliard type of gradient model, is considered under the restriction of miscibility. Weakly periodic boundary conditions on the pertinent fields provide the general variational setting for the uniquely solvable RVE-problem(s). These boundary conditions are introduced with a novel approach in order to control the stability of the boundary discretization, thereby circumventing the need to satisfy the LBB-condition: the penalty stabilized Lagrange multiplier formulation, which enforces stability at the cost of an additional Lagrange multiplier for each weakly periodic field (three fields for the current problem). In particular, a neat result is that the classical Neumann boundary condition is obtained when the penalty becomes very large. In the numerical examples, we investigate the following characteristics: the mesh convergence for different boundary approximations, the sensitivity for the choice of penalty parameter, and the influence of RVE-size on the macroscopic response