Modern software typically performs more than one functionality. These functionalities or features are not always organized in a way for modules representing these features to be used individually. Many software engineering approaches like programming language constructs, or product line visualization techniques have been proposed to organize projects as modules. Unfortunately, much legacy software suffer from years or decades of improper coding practices that leave the modules in the code almost undetectable. In such scenarios, a desirable requirement is to identify modules representing different features to be extracted. In this paper, we propose a novel approach that combines information retrieval and program analysis approaches to allow domain experts to identify slices of the program that represent modules using natural language search terms. We evaluate our approach by building a proof of concept tool in C, and extract modules from open source projects.

String obfuscation is an established technique used by proprietary, closed-source applications to protect intellectual property. Furthermore, it is also frequently used to hide spyware or malware in applications. In both cases, the techniques range from bit-manipulation over XOR operations to AES encryption. However, string obfuscation techniques/tools suffer from one shared weakness: They generally have to embed the necessary logic to deobfuscate strings into the app code. In this paper, we show that most of the string obfuscation techniques found in malicious and benign applications for Android can easily be broken in an automated fashion. We developed StringHound, an open-source tool that uses novel techniques that identify obfuscated strings and reconstruct the originals using slicing. We evaluated StringHound on both benign and malicious Android apps. In summary, we deobfuscate almost 30 times more obfuscated strings than other string deobfuscation tools. Additionally, we analyzed 100,000 Google Play Store apps and found multiple obfuscated strings that hide vulnerable cryptographic usages, insecure internet accesses, API keys, hard-coded passwords, and exploitation of privileges without the awareness of the developer. Furthermore, our analysis reveals that not only malware uses string obfuscation but also benign apps make extensive use of string obfuscation.

Software has become an indispensable part of industrial production and thus influences the life cycle of manufacturing systems, as many of these systems have to be replaced or evolved due to changing requirements. Software adaptation through continuous evolution extends the service time of these systems and thus saves valuable resources. In this paper we present an interdisciplinary methodology for reengineering legacy software to increase the productive lifetime. Our proposed approach systematically reuses implicit domain knowledge to evolve, validate and commission new software from legacy code. The approach is evaluated on a CNC machine as a special type of industrial system.

In order to achieve a higher reusability and testability, static analyses are increasingly being build as modular pipelines of analysis components. However, to build, debug, test, and evaluate these components the complete pipeline has to be executed every time. This process recomputes intermediate results which have already been computed in a previous run but are lost because the preceding process ended and removed them from memory. We propose to leverage runtime reusability for static analysis pipelines and introduce SootKeeper, a framework to modularize static analyses into OSGi (Open Service Gateway initiative) bundles, which takes care of the automatic caching of intermediate results. Little to no change to the original analysis is necessary to use SootKeeper while speeding up the execution of code-build-debug cycles or evaluation pipelines significantly.