Web API Fragility: How Robust is Your Web API Client

Web APIs provide a systematic and extensible approach for application-to-application interaction. A large number of mobile applications makes use of web APIs to integrate services into apps. Each Web API's evolution pace is determined by their respective developer and mobile application developers are forced to accompany the API providers in their software evolution tasks. In this paper we investigate whether mobile application developers understand and how they deal with the added distress of web APIs evolving. In particular, we studied how robust 48 high profile mobile applications are when dealing with mutated web API responses. Additionally, we interviewed three mobile application developers to better understand their choices and trade-offs regarding web API integration.Comment: Technical repor

Multi-Objective Improvement of Android Applications

Non-functional properties, such as runtime or memory use, are important to mobile app users and developers, as they affect user experience. Previous work on automated improvement of non-functional properties in mobile apps failed to address the inherent trade-offs between such properties. We propose a practical approach and the first open-source tool, GIDroid (2023), for multi-objective automated improvement of Android apps. In particular, we use Genetic improvement, a search-based technique that navigates the space of software variants to find improved software. We use a simulation-based testing framework to greatly improve the speed of search. GIDroid contains three state-of-the-art multi-objective algorithms, and two new mutation operators, which cache the results of method calls. Genetic improvement relies on testing to validate patches. Previous work showed that tests in open-source Android applications are scarce. We thus wrote tests for 21 versions of 7 Android apps, creating a new benchmark for performance improvements. We used GIDroid to improve versions of mobile apps where developers had previously found improvements to runtime, memory, and bandwidth use. Our technique automatically re-discovers 64% of existing improvements. We then applied our approach to current versions of software in which there were no known improvements. We were able to improve execution time by up to 35%, and memory use by up to 33% in these apps.Comment: 32 pages, 8 Figure

Overcoming Language Dichotomies: Toward Effective Program Comprehension for Mobile App Development

Mobile devices and platforms have become an established target for modern software developers due to performant hardware and a large and growing user base numbering in the billions. Despite their popularity, the software development process for mobile apps comes with a set of unique, domain-specific challenges rooted in program comprehension. Many of these challenges stem from developer difficulties in reasoning about different representations of a program, a phenomenon we define as a "language dichotomy". In this paper, we reflect upon the various language dichotomies that contribute to open problems in program comprehension and development for mobile apps. Furthermore, to help guide the research community towards effective solutions for these problems, we provide a roadmap of directions for future work.Comment: Invited Keynote Paper for the 26th IEEE/ACM International Conference on Program Comprehension (ICPC'18

TEGDroid: Test Case Generation Approach for Android Apps Considering Context and GUI Events

The advancement in mobile technologies has led to the production of mobile devices (e.g. smartphone) with rich innovative features. This has enabled the development of mobile applications that offer users an advanced and extremely localized context-aware content. The recent dependence of people on mobile applications for various computational needs poses a significant concern on the quality of mobile applications. In order to build a high quality and more reliable applications, there is a need for effective testing techniques to test the applications. Most existing testing technique focuses on GUI events only without sufficient support for context events. This makes it difficult to identify other defects in the changes that can be inclined by context in which an application runs. This paper presents an approach named TEGDroid for generating test case for Android Apps considering both context and GUI Events. The GUI and context events are identified through the static analysis of bytecode, and the analysis of app’s permission from the XML file. An experiment was performed on real world mobile apps to evaluate TEGDroid. Our experimental results show that TEGDroid is effective in identifying context events and had 65%-91% coverage across the eight selected applications. To evaluate the fault detection capability of this approach, mutation testing was performed by introducing mutants to the applications. Results from the mutation analysis shows that 100% of the mutants were killed. This indicates that TEGDroid have the capability to detect faults in mobile apps

Automatic Classification of Equivalent Mutants in Mutation Testing of Android Applications

Software and symmetric testing methodologies are primarily used in detecting software defects, but these testing methodologies need to be optimized to mitigate the wasting of resources. As mobile applications are becoming more prevalent in recent times, the need to have mobile applications that satisfy software quality through testing cannot be overemphasized. Testing suites and software quality assurance techniques have also become prevalent, which underscores the need to evaluate the efficacy of these tools in the testing of the applications. Mutation testing is one such technique, which is the process of injecting small changes into the software under test (SUT), thereby creating mutants. These mutants are then tested using mutation testing techniques alongside the SUT to determine the effectiveness of test suites through mutation scoring. Although mutation testing is effective, the cost of implementing it, due to the problem of equivalent mutants, is very high. Many research works gave varying solutions to this problem, but none used a standardized dataset. In this research work, we employed a standard mutant dataset tool called MutantBench to generate our data. Subsequently, an Abstract Syntax Tree (AST) was used in conjunction with a tree-based convolutional neural network (TBCNN) as our deep learning model to automate the classification of the equivalent mutants to reduce the cost of mutation testing in software testing of android applications. The result shows that the proposed model produces a good accuracy rate of 94%, as well as other performance metrics such as recall (96%), precision (89%), F1-score (92%), and Matthew’s correlation coefficients (88%) with fewer False Negatives and False Positives during testing, which is significant as it implies that there is a decrease in the risk of misclassification.publishedVersio