Software evolution analysis can shed light on various aspects of software development and maintenance. Up to date, there is little empirical evidence on the evolution of JavaScript (JS) applications in terms of maintainability and changeability, even though JavaScript is among the most popular scripting languages for front-end web applications. In this study, we investigate JS applications’ quality and changeability trends over time by examining the relevant Laws of Lehman. We analyzed over 7,500 releases of JS applications and reached some interesting conclusions. The results show that JS applications continuously change and grow, there are no clear signs of quality degradation while the complexity remains the same over time, despite the fact that the understandability of the code deteriorates.

Managing the maintenance process and estimating accurately the effort and duration required for a new release is considered to be a crucial task as it affects successful software project survival and progress over time. In this study, we propose the combination of two well-known machine learning (ML) techniques, Bayesian Networks (BNs), and Association Rules (ARs) for modeling the maintenance process by identifying the relationships among the internal and external quality metrics related to a particular project release to both the maintainability of the project and the maintenance process indicators (i.e., effort and duration). We also exploit Bayesian inference, to test the effect of certain changes in internal and external project factors to the maintainability of a project. We evaluate our approach through a case study on 957 releases of five open source JavaScript applications. The results show that the maintainability of a release, the changes observed between subsequent releases, and the time required between two releases can be accurately predicted from size, complexity, and activity metrics. The proposed combined approach achieves higher accuracy when evaluated against the BN model accuracy.

This paper proposes a cloud application architecture called OLEA, for monitoring the olive oil production chain. OLEA system deployment follows adivide–and-conquer management logic, which maintains olive tree clusters. On each cluster, NFC technology is used for monitoring plant protection practices and fertilization. Apart from on-site monitoring services, the system is also equipped with virgin oil management services. It uses an OLEA system controller that interconnects with sensors on oil mills, for the procurement of quantitative and qualitative olive oil characteristics, during the industrial extraction process. OLEAsystem services and management algorithms are controlled by a cloud application server, where collected data uploads and notifications are sent to the end users using a mobile phone application. This paper presents the OLEA system technical characteristics as well as the structure of OLEA communication protocols. Furthermore, a case study of the OLEA system data mining capabilities is presented examining the application of such efforts to the improvement of systematic cultivation, branding and product exports.

Several IoT middleware protocols have been proposed for the wireless IoT data transfer. The main representatives are the Constrained Application Protocol(CoAP), the Simple Object Access Protocol (SOAP), the message queuing Telemetry Transport (MQTT) and the HypertextTransfer Protocol (HTTP). Protocols deployment constraints are the message delay, message loss, processing effort and power consumption that IoT devices demand, in order to successfully transfer wireless data. In exchange for the reduction of device energy consumption, many of these IoT protocols try to lower the data throughput, minimize security, or even limit coverage. In this paper authors compare the performance of IoT application protocols using Machine to Machine (M2M) delay scenarios measuring the extra effort that they enforce to the transmitted data. Experimentation results reveal which protocol is best suited for different network and application scenarios accordingly.

The use of t-Distributed Stochastic Neighborhood Embedding (t-SNE) for the visualization of multidimensional data has proven to be a popular approach, with applications published in a wide range of domains. Despite their usefulness, t-SNE plots can sometimes be hard to interpret or even misleading, which hurts the trustworthiness of the results. By opening the black box of the algorithm and showing insights into its behavior through visualization, we may learn how to use it in a more effective way. In this work, we present t-viSNE, a visual inspection tool that enables users to explore anomalies and assess the quality of t-SNE results by bringing forward aspects of the algorithm that would normally be lost after the dimensionality reduction process is finished.