Maintainability is one of the most important quality attribute that affect the quality of software. There are four factors that affect the maintainability of software which are: analyzability, changeability, stability, and testability. Open source software (OSS) developed by collaborative work done by volunteers through around the world with different management styles. Open source code is updated and modified all the time from the first release. Therefore, there is a need to measure the quality and specifically the maintainability of such code. This paper discusses the maintainability for the three domains of the open source software. The domains are: education, business and game. Moreover, to observe the most effective metrics that directly affects the maintainability of software. Analysis of the results demonstrates that OSS in the education domain is the most maintainable code and cl_stat (number of executable statements) metric has the highest degree of influence on the calculation of maintenance in all three domains.

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Software testing is the main step of detecting the faults in Software through executing it. Therefore, it is substantial to predict the faults that may happen while executing the software to maintain the existence of the software. There are different techniques of artificial intelligence that are utilized to predict future defects. The Machine learning is one of the most significant technique that used to build predicting models. In this paper, conducted a systematic review of the supervised machine learning techniques which are used for software defect prediction and evaluated the performance. Thus, using five state-of-the-art supervised machine learning (classifiers), for the evaluation, several of the data are used to predict software fault. In addition to, compared the performance of these classifiers with various parameters. After that, proceeds many experiments to improve the efficiency of the prediction of the defect through modifying the default parameters of the classifier. The results showed the ability of supervised machine learning algorithms to classify classes as bugs or not bugs. Thus, using supervised machine learning models for predicting software bugs is better than the traditional statistical models. Additionally, using PCA never noticeable impact on prediction systems performance while modifying the default parameters positively impact classifier values, especially with Artificial Neural Network (ANN).The main finding of this paper is gained through the application of Ensemble Learning methods, whereas Bagging achieves 95.1% accuracy with Mozilla dataset and Voting achieves 93.79% accuracy with kc1 dataset.

The growth of mobile commerce marketplaces worldwide has been boosted by modern advances in digital technology. However, Privacy and security are still concern in m-commerce application. Since the previous study has researched the link between security and privacy and purpose to use, the factors that influence the formation of privacy and security in m-commerce are mostly unidentified. On the basis of UTAUT2, this study investigates the factors of security and privacy effecting mobile commerce acceptance. A hybrid SEM-ANN method was utilized to identify non-linear and compensatory interactions. Compensatory and Linear models are based on the idea that a deficiency in one component might also be compensated for by other variables. Linear and Non-compensatory models, on the other hand, seem to overcomplicate buyer decision mechanisms. Survey criteria have been conducted to obtain 890 mobile commerce consumer’s datasets utilizing an application on m-commerce. The following are the results. (1) M-commerce acceptability and use were positively influenced by five determinants (Security, performance expectancy, effort expectancy, habit, and price value). (2) Un-authorization, Error, secondary usage, collection, control, and awareness were all shown to directly and significantly negatively impact M-COMMERCE acceptance and use. (3) Three additional variables (social influence, hedonic motivation, and facilitating conditions) did not affect customers' intentions to use m-commerce applications in Jordan. In m-commerce, the integrated model expects a 45% percent increase in security and privacy.