LogPruner: detect, analyze and prune logging calls in Android apps

Zhou, Xin; Wu, Kaidong; Cai, Huaqian; Lou, Shuai; Zhang, Ying; Huang, Gang

doi:10.1007/s11432-017-9354-x

Cited by 6 publications

(2 citation statements)

References 2 publications

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“…Song et al (2017) used RE for improving the security of Android applications. While Zhou et al (2018) used the RE technique to detect logging classes and to remove logging calls and unnecessary instructions. Also, Arnatovich et al (2018) used RE to perform program analysis on a textual form of the executable source and to represent it with an intermediate language (IL).…”

Section: Related Workmentioning

confidence: 99%

Reverse engineering approach for improving the quality of mobile applications

Elsayed

ElDahshan

El-Sharawy

et al. 2019

PeerJ Computer Science

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BackgroundPortable-devices applications (Android applications) are becoming complex software systems that must be developed quickly and continuously evolved to fit new user requirements and execution contexts. Applications must be produced rapidly and advance persistently in order to fit new client requirements and execution settings. However, catering to these imperatives may bring about poor outline decisions on design choices, known as anti-patterns, which may possibly corrupt programming quality and execution. Thus, the automatic detection of anti-patterns is a vital process that facilitates both maintenance and evolution tasks. Additionally, it guides developers to refactor their applications and consequently enhance their quality.MethodsWe proposed a general method to detect mobile applications’ anti-patterns that can detect both semantic and structural design anti-patterns. The proposed method is via reverse-engineering and ontology by using a UML modeling environment, an OWL ontology-based platform and ontology-driven conceptual modeling. We present and test a new method that generates the OWL ontology of mobile applications and analyzes the relationships among object-oriented anti-patterns and offer methods to resolve the anti-patterns by detecting and treating 15 different design’s semantic and structural anti-patterns that occurred in analyzing of 29 mobile applications. We choose 29 mobile applications randomly. Selecting a browser is not a criterion in this method because the proposed method is applied on a design level. We demonstrate a semantic integration method to reduce the incidence of anti-patterns using the ontology merging on mobile applications.ResultsThe proposed method detected 15 semantic and structural design anti-patterns which have appeared 1,262 times in a random sample of 29 mobile applications. The proposed method introduced a new classification of the anti-patterns divided into four groups. “The anti-patterns in the class group” is the most group that has the maximum occurrences of anti-patterns and “The anti-patterns in the operation group” is the smallest one that has the minimum occurrences of the anti-patterns which are detected by the proposed method. The results also showed the correlation between the selected tools which we used as Modelio, the Protégé platform, and the OLED editor of the OntoUML. The results showed that there was a high positive relation between Modelio and Protégé which implies that the combination between both increases the accuracy level of the detection of anti-patterns. In the evaluation and analyzing the suitable integration method, we applied the different methods on homogeneous mobile applications and found that using ontology increased the detection percentage approximately by 11.3% in addition to guaranteed consistency.

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Section: Related Workmentioning

confidence: 99%

Reverse engineering approach for improving the quality of mobile applications

Elsayed

ElDahshan

El-Sharawy

et al. 2019

PeerJ Computer Science

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“…The vast accumulation of source code and related documentation (i.e., big data in software development) has boosted data-driven software automation [18] for a variety of tasks such as code recommendation [4,6,8], program generation [19] and optimization [20,21]. Besides the approaches mentioned in the previous sections, this study is related to the following studies.…”

Section: Related Workmentioning

confidence: 99%

Generative API usage code recommendation with parameter concretization

Chen

Peng

Sun

et al. 2019

Sci. China Inf. Sci.

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Many programming languages and development frameworks have extensive libraries (e.g., JDK and Android libraries) that ease the task of software engineering if used effectively. With numerous library classes and sometimes intricate API (application programming interface) usage constraints, programmers often have difficulty remembering the library APIs and/or using them correctly. This study addresses this problem by developing an engine called DeepAPIRec, which automatically recommends the API usage code. Compared to the existing proposals, our approach distinguishes itself in two ways. First, it is based on a tree-based long short-term memory (LSTM) neural network inspired by recent developments in the machinelearning community. A tree-based LSTM neural network allows us to model and reason about variable-length, preceding and succeeding code contexts, and to make precise predictions. Second, we apply data-flow analysis to generate concrete parameters for the API usage code, which not only allows us to generate complete code recommendations but also improves the accuracy of the learning results according to the tree-based LSTM neural network. Our approach has been implemented for supporting Java programs. Our experimental studies on the JDK library show that at statement-level recommendations, DeepAPIRec can achieve a top-1 accuracy of about 37% and a top-5 accuracy of about 64%, which are significantly better than the existing approaches. Our user study further confirms that DeepAPIRec can help developers to complete a segment of code faster and more accurately as compared to IntelliJ IDEA.

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