No Arabic abstract
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.
In modern programming languages, exception handling is an effective mechanism to avoid unexpected runtime errors. Thus, failing to catch and handle exceptions could lead to serious issues like system crashing, resource leaking, or negative end-user experiences. However, writing correct exception handling code is often challenging in mobile app development due to the fast-changing nature of API libraries for mobile apps and the insufficiency of their documentation and source code examples. Our prior study shows that in practice mobile app developers cause many exception-related bugs and still use bad exception handling practices (e.g. catch an exception and do nothing). To address such problems, in this paper, we introduce two novel techniques for recommending correct exception handling code. One technique, XRank, recommends code to catch an exception likely occurring in a code snippet. The other, XHand, recommends correction code for such an occurring exception. We have developed ExAssist, a code recommendation tool for exception handling using XRank and XHand. The empirical evaluation shows that our techniques are highly effective. For example, XRank has top-1 accuracy of 70% and top-3 accuracy of 87%. XHands results are 89% and 96%, respectively.
Enterprise Mobility has been increasing the reach over the years. Initially Mobile devices were adopted as consumer devices. However, the enterprises world over have rightly taken the leap and started using the ubiquitous technology for managing its employees as well as to reach out to the customers. While the Mobile ecosystem has been evolving over the years, the increased exposure of mobility in Enterprise framework have caused major focus on the security aspects of it. While a significant focus have been put on network security, this paper discusses on the approach that can be taken at Mobile application layer, which would reduce the risk to the enterprises.
A mobile app interface usually consists of a set of user interface modules. How to properly design these user interface modules is vital to achieving user satisfaction for a mobile app. However, there are few methods to determine design variables for user interface modules except for relying on the judgment of designers. Usually, a laborious post-processing step is necessary to verify the key change of each design variable. Therefore, there is a only very limited amount of design solutions that can be tested. It is timeconsuming and almost impossible to figure out the best design solutions as there are many modules. To this end, we introduce FEELER, a framework to fast and intelligently explore design solutions of user interface modules with a collective machine learning approach. FEELER can help designers quantitatively measure the preference score of different design solutions, aiming to facilitate the designers to conveniently and quickly adjust user interface module. We conducted extensive experimental evaluations on two real-life datasets to demonstrate its applicability in real-life cases of user interface module design in the Baidu App, which is one of the most popular mobile apps in China.
This paper presents an approach towards specifying and verifying adaptive distributed systems. We here take fault-handling as an example of adaptive behavior and propose a modeling language Sandal for describing fault-prone message-passing systems. One of the unique mechanisms of the language is a linguistic support for abstracting typical faults such as unexpected termination of processes and random loss of messages. The Sandal compiler translates a model into a set of NuSMV modules. During the compilation process, faults specified in the model will be woven into the output. One can thus enjoy full-automatic exhaustive fault-injection without writing faulty behaviors explicitly. We demonstrate the advantage of the language by verifying a model of the two-phase commit protocol under faulty environment.
The FermaT transformation system, based on research carried out over the last sixteen years at Durham University, De Montfort University and Software Migrations Ltd., is an industrial-strength formal transformation engine with many applications in program comprehension and language migration. This paper is a case study which uses automated plus manually-directed transformations and abstractions to convert an IBM 370 Assembler code program into a very high-level abstract specification.