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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Ontology is the core of the semantic applications, so the quality of it has a direct proportion with Ontology. It impacts directly on the long life of the semantic applications. There are different negative effects in the design of Ontology' classes as Blob, Lazy Class, Large Class, and Singleton. These negative effects called bad smells. However, detecting smells is not supported in any Ontology editors. This paper proposes a metric method called ONTOPYTHO. It can detect classes' smells automatically from Ontology models even if it is a Big Ontology. We programed ONTOPYTHO via Python and SPARQL languages. We evaluated the proposed method ONTOPYTHO by applying it on twelve publicly OWL Ontology projects. The detected smells appeared 117495 times in the twelve projects. The results showed that both the size and the number of classes of OWL Ontology has no effect in the presence of the smells. The results also showed that 69.24% of the classes are lazy classes. This means that big OWL Ontologies are not big in their nature, but because of theses lazy classes. The proposed method is the first method that detects Lazy class smell in the design of big OWL Ontology. In our random sample of big Ontologies, Lazy class smell appears approximately 99.8% of the smells.

Background: security has become a major concern for smartphone users in line with the increasing use of mobile applications, which can be downloaded from unofficial sources. These applications make users vulnerable to penetration and viruses. Malicious software (malware) is unwanted software that is frequently used by cybercriminals to launch cyber-attacks. Therefore, the motive of the research was to detect malware early before infection by discovering it at the application-design level and not at the code level, where the virus will have already damaged the system. Methods: in this article, we proposed a malware detection method at the design level based on reverse engineering, the unified modeling language (UML) environment, and the web ontology language (OWL). The proposed method detected “Data_Send_Trojan” malware by designing a UML model that simulated the structure of the malware. Then, by generating the ontology of the model, and using RDF query language (SPARQL) to create certain queries, the malware was correctly detected. In addition, we proposed a new classification of malware that was suitable for design detection. Results: the proposed method detected Trojan malware that appeared 552 times in a sample of 600 infected android application packages (APK). The experimental results showed a good performance in detecting malware at the design level with precision and recall of 92% and 91%, respectively. As the dataset increased, the accuracy of detection increased significantly, which made this methodology promising.

Malicious software (malware) can steal passwords, leak details, and generally cause havoc with users’ accounts. Most of the current malware detection techniques are designed to detect malware at the code level of the software, where it is actually infected and causes damage. Additionally, current malware detection techniques at the design level are done manually or semi-automatically. This research aims to enhance these methods to detect malware at the design level automatically with a big dataset. The proposed method presents an automatic system for detecting SMS (Short Message Service) malware at the design which is called APKOWL. It is based on reverse engineering of the mobile application and then automatically builds OWL (web ontology Language) ontology. The proposed system is implemented in python and Protégé, and its performance has been tested and evaluated on samples of android mobile applications including 3,904 malware and 3,200 benign samples. The experimental results successfully verify the effectiveness of the proposed method because it has good performance in detecting SMS malware at the software design level. The proposed method obtained an accuracy of 97%, precision of 97.5%, and recall of 99%, outperforming the compared model in all performance metrics.