Most modern UAVs/drones have a dependency on using a mobile device as part of the flight systems.The drone manufacturers launch the controlling app for the drones in mobile app stores.There has been a tremendous upsurge in the number of UAVs (Unmanned Aerial Vehicle) mobile apps on the Google Play Store over the past few years.A UAV/Drone app user expresses the experience with the app by providing comments on the Google Play Store.Whereas, the developers interact with the user by posting replies to users' comments.Feedback in users' comments enables developers of mobile apps to discover issues, such as features requests, bugs to fix, and app maintenance requests, in a timely manner.The value of responding to a user review of drone apps has never been explored.We conducted the largest and most extensive empirical study of UAV mobile apps todate by analyzing the 1,825 UAV mobile apps across twenty-five categories, with 162,250 reviews (user comments and developers' replies).We categorized the developer replies into seven major categories.We also find that 35% of the developer's replies are associated with providing direct solutions to the drone app user's problems.Whereas, only 1% of the developer replies are related to the app's monetary refund issues.

When a disaster strikes, such as a flood, it is difficult, even impossible for transportation authority and first responders to get access to real-time information of every structure (highway bridges and connected roads) in a timely fashion. The information is needed to perform short-term predictions and support emergency/rescue activities. To mitigate the challenge, the proposed research aims to develop an autonomous and real-time structural health monitoring system (SHM) using low cost off-the-shelf wireless sensors. The wireless sensors upon deployment will collaborate to form an autonomous wireless sensor network (WSN) and relay a high-resolution structural health data to transport engineers in real-time. Such up-to-date information can assist the department of transportation (DOT), the city, first responders as well as rescues authorities such as 911 to determine the sensitive spots and prioritize recovery activities intended to rescue lives, restore essential services and identify repair damages requiring an emergency fix.

The popularity of smartphones - small computers that run on battery power - has exploded in the last decade. Unsurprisingly, power consumption is an overarching concern for mobile app developers, who are anxious to learn about power-related problems that are encountered by others. In this paper, we present an empirical study exploring the characteristics of energy-related questions posed in Stack Overflow, issues faced by the developers, and the most significantly discussed APIs. We extracted a sample of 5009 Stack Overflow questions, and manually analyzed 1000 posts of Android-related energy questions. Our study shows that developers are most concerned about energy-related issues that concern improper implementations, sensor, and radio utilization.

API design is known to be a challenging craft, as API designers must balance their elegant ideals against "real-world" concerns, such as utility, performance, backwards compatibility, and unforeseen emergent uses. However, to date, there is no principled method to collect or analyze API usability information that incorporates input from typical developers. In practice, developers often turn to Q&A websites such as stackoverflow.com (SO) when seeking expert advice on API use, the popularity of such sites has thus led to a very large volume of unstructured information that can be searched with diligence for answers to specific questions. The collected wisdom within such sites could, in principle, be of great help to API designers to better support developer needs, if only it could be collected, analyzed, and distilled for practical use. In this paper, we present a methodology that combines several techniques, including social network analysis and topic mining, to recommend SO posts that are likely to concern API design-related issues. To establish a comparison baseline, we introduce two more recommendation approaches: a reputation-based recommender and a random recommender. We have found that when applied to Q&A discussion of two popular mobile platforms, Android and iOS, our methodology achieves up to 93% accuracy and is more stable with its recommendations when compared to the two baseline techniques.

Load testing is one of the means for evaluating the performance of Large Scale Systems (LSS). At the end of a load test, performance analysts must analyze thousands of performance counters from hundreds of machines under test. These performance counters are measures of run-time system properties such as CPU utilization, Disk I/O, memory consumption, and network traffic. Analysts observe counters to find out if the system is meeting its Service Level Agreements (SLAs). In this paper, we present and evaluate one supervised and three unsupervised approaches to help performance analysts to 1) more effectively compare load tests in order to detect performance deviations which may lead to SLA violations, and 2) to provide them with a smaller and manageable set of important performance counters to assist in root-cause analysis of the detected deviations. Our case study is based on load test data obtained from both a large scale industrial system and an open source benchmark application. The case study shows, that our wrapper-based supervised approach, which uses a search-based technique to find the best subset of performance counters and a logistic regression model for deviation prediction, can provide up to 89% reduction in the set of performance counters while detecting performance deviations with few false positives (i.e., 95% average precision). The study also shows that the supervised approach is more stable and effective than the unsupervised approaches but it has more overhead due to its semi-automated training phase.

Enterprise systems are load tested for every added feature, software updates and periodic maintenance to ensure that the performance demands on system quality, availability and responsiveness are met. In current practice, performance analysts manually analyze load test data to identify the components that are responsible for performance deviations. This process is time consuming and error prone due to the large volume of performance counter data collected during monitoring, the limited operational knowledge of analyst about all the subsystem involved and their complex interactions and the unavailability of up-to-date documentation in the rapidly evolving enterprise. In this paper, we present an automated approach based on a robust statistical technique, Principal Component Analysis (PCA) to identify subsystems that show performance deviations in load tests. A case study on load test data of a large enterprise application shows that our approach do not require any instrumentation or domain knowledge to operate, scales well to large industrial system, generate few false positives (89% average precision) and detects performance deviations among subsystems in limited time.

Performance analysts rely heavily on load testing to measure the performance of their applications under a given load. During the load test, analyst strictly monitor and record thousands of performance counters to measure the run time system properties such as CPU utilization, Disk I/O, memory consumption, network traffic etc. The most frustrating problem faced by analysts is the time spent and complexity involved in analysing these huge counter logs and finding relevant information distributed across thousands of counters. We present our methodology to help analysts by automatically identifying important performance counters for load test and comparing them across tests to find performance gain/loss. Further, our methodology help analysts to understand the root cause of a load test failure by finding previously solved problems in test repositories. A case study on load test data of a large enterprise application shows that our methodology can effectively guide performance analysts to identify and compare top performance counters across tests in limited time thereby archiving 88% counter data reduction.

Large scale systems (LSS) contain multiple subsystems that interact across multiple nodes in sometimes unforeseen and complicated ways. As a result, pinpointing the subsystems that are the source of performance degradation for a load test in LSS can be frustrating, and might take several hours or even days. This is due to the large volume of performance counter data collected such as CPU utilization, Disk I/O, memory consumption and network traffic, the limited operational knowledge of analysts about all subsystems of an LSS and the unavailability of up-to-date documentation in a LSS. We have developed a methodology that automatically ranks the subsystems according to the deviation of their performance in a load test. Our methodology uses performance counter data of a load test to craft performance signatures for the LSS subsystems. Pair-wise correlations among the performance signatures of subsystems within a load test are compared with the corresponding correlations in a baseline test to pinpoint the subsystems responsible for the performance violations. Case studies on load test data obtained from a large telecom system and that of an open source benchmark application show that our approach provides an accuracy of 79% and do not require any instrumentation or domain knowledge to operate.

Shrinking size and increasing deployment density of wireless sensor nodes implies to smaller equipped battery size. This means emerging wireless sensor nodes must compete for efficient energy utilization. MAC protocols play a vital role in energy consumption of sensor node as it controls the radio activities. Customized or open source simulators play an important role to measure the performance effectiveness of MAC protocols. Due to the fact that they are flexible, reduce experimental overhead and cost. Nevertheless, these benefits come at the cost of results accuracy. In this paper, we investigate differences of the behaviour of our agent based S-MAC protocols in real deployment compared to the results produced using our custom based simulator, which ignores the lower layers effects such as packet collision and overhearing and using an ns2, an open source simulator, which provides a complete protocol stack. We further try to find and explain the rationale of the variance of results produced by real deployment and that of simulators.

Up-to-date comments are critical for the successful evolution of a software application. When modifying a function, developers may update the comment associated with the function or may not update it. For example, comments associated with a complex function are likely to be updated more often when the function is modified to prevent the code and the comments from drifting apart. Nevertheless, the rationale behind updating a comment has never been studied. In this paper, we present a large empirical study to better understand the rationale for updating comments. We recover the code change history for four large open source projects (GCC: a compiler, FreeBSD: an operation system, PostgreSQL: a database management system, and GCluster: a clustering framework) with an average code history of 10 years. Using the Random Forests algorithm, we investigate the rationale for updating comments along three dimensions: characteristics of the changed function, characteristics of the change itself and time and code ownership characteristics. Our case study shows that we can predict with an accuracy of 80%; the likelihood of updating the comment associated with a modified function. We perform a sensitivity analysis to determine the most important attributes. Our analysis shows that the percentage of changed call dependencies and control statements, the age of the modified function and the number of co-changed functions which depend on it are the most important attributes in determining the likelihood of updating comments.

When changing a source code entity (e.g., a function), developers must ensure that the change is propagated to related entities to avoid the introduction of bugs. Accurate change propagation is essential for the successful evolution of complex software systems. Techniques and tools are needed to support developers in propagating changes. Several heuristics have been proposed in the past for change propagation. Research shows that heuristics based on the change history of a project outperform heuristics based on the dependency graph. However, these heuristics being static are not the answer to the dynamic nature of software projects. These heuristics need to adapt to the dynamic nature of software projects and must adjust themselves for the peculiarities of each changed entity. In this paper we propose adaptive change propagation heuristics. These heuristics are metaheuristics that combine various previously researched heuristics to improve the overall performance (precision and recall) of change propagation heuristics. Through an empirical case study, using four large open source systems; GCC (a compiler), FreeBSD (an operating system), PostgreSQL (a database), and GCluster (a clustering framework), we demonstrate that our adaptive change propagation heuristics show a 57% statistically significant improvement over the top-performing static change propagation heuristics.

Most existing message-oriented middleware for mobile networks rely on infrastructure or assume the existence of reliable and continuous connections among communicating entities. These schemes do not take advantage of cross-layer optimizations. Publish/subscribe middleware has been widely used to provide asynchronous communication functionality in distributed application environments. Due to node mobility and frequent node disconnections, asynchronous publish/subscribe message middleware is a proper communication model for mobile ad hoc networking environments. In this paper, we propose a MOBility-aware and CROSS-layer based middleware (MOBCROSS) for mobile ad hoc networks. MOBCROSS is based on the publishing and subscription of topics. The main features of MOBCROSS are mobility awareness, network reconfiguration, and cross-layer interaction support. The performance of the middleware is evaluated in a simulation environment. The simulation results indicated that MOBCROSS achieves a good packet delivery ratio and low overhead.

This paper presents an agent-based system to minimize the energy consumption for border nodes in sensor-MAC (S-MAC), a cluster based contention protocol. The S-MAC protocol is based on unique feature; it conserves battery power at nodes by powering off nodes that are not actively transmitting or receiving packets. In doing so, nodes also turn off their radios. Inspired by the energy conservation mechanism of the S-MAC, we unmitigated our efforts to augment the node life time in sensor network. Border nodes act as shared nodes between virtual clusters. Virtual clusters are formed on the basis of sleep/listen schedule of nodes. Towards this end, we propose a multi-agent system that allows nodes to join cluster where they experience minimum energy drain. This system includes two types of agents: stationary and mobile agents. A prototype implementation and simulation results compared with S-MAC are presented.

In an environment where node density is massive, placement is heterogeneous and lots of sensory traffic with redundancy is produced; individual nodes waste limited wireless bandwidth and consume lot of energy. This minimizes their life time on the network. To handle such problem number of routing schemes have been proposed by researchers. These schemes tend to find the minimum energy path to prolong the network life. In this paper, we argue that it is not always wise to use the minimumenergy path. Nodes along the optimal path will be used rapidly, burn out energy aggressively and eventually die hastily creating communication holes in network. This brings rapid change in the topology resulting in increased latency, poor connectivity and production of heterogeneous subnets. Thus, it is better to occasionally use suboptimal paths. We unmitigated our efforts to augment the node life time in sensor network by frequent use of suboptimal paths, and reducing redundant sensory network traffic. Towards this end, we propose an agentbased directed diffusion approach that incorporates static and mobile agents. Static agent is responsible for calculating and maintaining the set of optimal paths.Mobile agent main function is to perform data processing and make data aggregation decisions at nodes in the network rather than bring data back to a central processor (sink). This paper provides an analytical model for data dissemination for both approaches. To demonstrate the performance evaluation, a prototype of a simulator is implemented.