MaintainoMATE: A GitHub App for Intelligent Automation of Maintenance Activities

Software development projects rely on issue tracking systems at the core of tracking maintenance tasks such as bug reports, and enhancement requests. Incoming issue-reports on these issue tracking systems must be managed in an effective manner. First, they must be labelled and then assigned to a particular developer with relevant expertise. This handling of issue-reports is critical and requires thorough scanning of the text entered in an issue-report making it a labor-intensive task. In this paper, we present a unified framework called MaintainoMATE, which is capable of automatically categorizing the issue-reports in their respective category and further assigning the issue-reports to a developer with relevant expertise. We use the Bidirectional Encoder Representations from Transformers (BERT), as an underlying model for MaintainoMATE to learn the contextual information for automatic issue-report labeling and assignment tasks. We deploy the framework used in this work as a GitHub application. We empirically evaluate our approach on GitHub issue-reports to show its capability of assigning labels to the issue-reports. We were able to achieve an F1-score close to 80\%, which is comparable to existing state-of-the-art results. Similarly, our initial evaluations show that we can assign relevant developers to the issue-reports with an F1 score of 54\%, which is a significant improvement over existing approaches. Our initial findings suggest that MaintainoMATE has the potential of improving software quality and reducing maintenance costs by accurately automating activities involved in the maintenance processes. Our future work would be directed towards improving the issue-assignment module

Combining data structure repair and program repair

textBugs in code continue to pose a fundamental problem for software reliability and cause expensive failures. The process of removing known bugs is termed debugging, which is a classic methodology commonly performed before code is deployed. Traditionally, debugging is tedious, often requiring much manual effort. A more recent technique that complements debugging is data structure repair, which handles bugs that make it to deployed systems and lead to erroneous behavior at runtime by modifying erroneous program states to recover from errors. While data structure repair presents a promising basis for dealing with bugs at runtime, it remains computationally expensive. Our thesis is that debugging and data structure repair can be integrated to provide the basis of an effective approach for removing bugs before code is deployed and handling them after it is deployed. We present a bi-directional integration where ideas at the basis of data structure repair assist in automating debugging and vice versa. Our key insight is two-fold: (1)a repair action performed to mutate an erroneous object field value to repair it can be abstracted into a program statement that performs that update correctly; and (2)repair actions that are performed repeatedly to fix the same error can be memoized and re-used. We design, develop, and evaluate two techniques that embody our insight. One, we present an automated debugging technique that leverages a systematic constraint-based data structure repair technique developed in previous work and provides suggestions on how to fix a faulty program. Two, we present repair abstractions that are based on the same central ideas as in our automated debugging technique and memoize how an erroneous state was repaired, which enables prioritizing and re-using repair actions when the same error occurs again. The focus of our work is programs that operate on structurally complex data, e.g., heap-allocated data structures that have complex structural integrity constraints, such as acyclicity. Checking such constraints plays a central role in the techniques that lay at the foundation of our work. These techniques require the user to provide the constraints, which poses a burden on the user. To facilitate the use of constraint-based techniques, we present a third technique to check constraint violations at runtime using graph spectra, which have been studied extensively by mathematicians to capture properties of graphs. We view the heap of an object-oriented program as an edge-labeled graph, which allows us to apply results from graph spectra theory. Experimental results show the effectiveness of using graph spectra as a basis of capturing structural properties of a class of commonly used data structures.Electrical and Computer Engineerin

Security vulnerabilities and cyber threat analysis of the AMQP protocol for the internet of things

The Internet of Things (IoT) expands the global Internet-connected network to encompass device-to-device, device-to-server, and server-to-server connectivity for an ever-increasing variety of end-user devices. IoT remains a somewhat amorphous entity, with little in the way of coordinated development, and is undermined largely by a manufacturer-driven scramble to be first-to-market with the latest innovation. Communication between IoT devices/servers relies on underlying protocols, which must be efficient and effective to establish and maintain reliability and integrity of data transfer. However, the lack of coordination during IoT’s expansion has resulted in a variety of communications protocols being developed. AMQP (Advanced Message Queuing Protocol) originated from the financial sector’s requirement for an improved messaging system that was fast, reliable and independent of end-user platform configurations. AMQP is an open-source server-to-server communications protocol which allows the addition of user-specific extensions. The software coding of such end-user-developed modules can be insufficient regarding threat-mitigation and can make the end product vulnerable to cyber-attack. Through this paper, we present vulnerability and threat analysis for AMQP-based IoT systems

Playing Doom with Anticipator-A3C Based Agents Using Deep Reinforcement Learning and the ViZDoom Game-AI Research Platform

The built-in game agents act according to the pre-written scripts and make decisions, take actions like they have been stated. They acquire and take advantage of unfair information, instead of acting flexibly like human players, who make decisions only based on game screens. This chapter focuses on studying the application of Deep Learning and Reinforcement Learning in games agents and the improvement of the related algorithms. The goal is to develop a game agent that makes decisions in human’s way and gets rid of relying on unfair information. A game agent (CNN) is implemented by augmenting the A3C algorithm. This agent takes the original real-time game screen as the input of the network, and then output the matching policy. The agent interacts with ViZDoom and reads the real-time game screen to make decisions for controlling the character to act. This chapter improved the A3C algorithm by adding an anticipator network to the original model structure. The goal of doing this is to make the agent act more like human players. It will generate anticipation before making decisions, then combine the real-time game screen with anticipation images together as a whole input of the network defined by the A3C algorithm. It can use the combination of the data to make decisions and output the discrete actions. Because the method only changes the structure of data for the input of the network, so it is a model-free method and can be easily transplanted to other algorithms. The performance of A3C is compared with variants proposed in this chapter, analyzed the differences between them and gathered the experimental data from the latest articles as a comparison which studies the same problem. The result shows, that the A3C algorithm with Anticipation performs better than the A3C algorithm

Linking social perception and provision of ecosystem services in a sprawling urban landscape: a case study of Multan, Pakistan

Urban sprawl causes changes in land use and a decline in many ecosystem services. Understanding the spatial patterns of sprawl and exploration of citizens’ perception towards the sporadic urban expansion and its impacts on an ecosystem to deliver services can help to guide land use planning and the conservation of the urban ecosystem. Here, we spatially examined land use changes in Multan, Pakistan, and investigated public perception about urban sprawl and its impacts on the quality and provision of ecosystem services, using a survey instrument. The spatial analysis of the historical land cover of Multan indicated an exponential expansion of the city in the last decade. Large areas of natural vegetation and agricultural land were converted to urban settlements in the past two decades. The citizens of Multan believe that the quality and provision of ecosystem services have declined in the recent past and strongly correlate the deteriorating ecosystem services with urban sprawl. Education and income levels of the respondents are the strongest predictors of urban ecosystem health literacy. Citizens associated with laborious outdoor jobs are more sensitive to the changes in ecosystem services. We concluded that the rapidly expanding cities, especially in the tropical arid zones, need to be prioritized for an increase in vegetation cover, and economically vulnerable settlements in these cities should be emphasized in climate change mitigation campaigns

Bioequivalence evaluation of new microparticulate capsule and marketed tablet dosage forms of lornoxicam in healthy volunteers

Purpose: To compare oral bioavailability and pharmacokinetic parameters of different lornoxicam formulations and to assess similarity in plasma level profiles by statistical techniques.Methods: An open-label, two-period crossover trial was followed in 24 healthy Pakistani volunteers (22 males, 2 females). Each participant received a single dose of lornoxicam controlled release (CR) microparticles and two doses (morning and evening) of conventional lornoxicam immediate release (IR) tablet formulation. The microparticles were prepared by spray drying method. The formulations were administered again in an alternate manner after a washout period of one week. Pharmacokinetic parameters were determined by Kinetica 4.0 software using plasma concentration-time data. Moreover, data were statistically analyzed at 90 % confidence interval (CI) and Schuirmann’s two one-sided t-test procedure.Results: Peak plasma concentration (Cmax) was 20.2 % lower for CR formulation compared to IR formulation (270.90 ng/ml vs 339.44 ng/ml, respectively) while time taken to attain Cmax (tmax) was 5.25 and 2.08 h, respectively. Area under the plasma drug level versus time (AUC) curve was comparable for both CR and IR formulations. The 90 % confidence interval (CI) values computed for Cmax, AUC0-24, and AUC0- , after log transformation, were 87.21, 108.51 and 102.74 %, respectively, and were within predefined bioequivalence range (80 - 125 %).Conclusion: The findings suggest that CR formulation of lornoxicam did not change the overall pharmacokinetic properties of lornoxicam in terms of extent and rate of lornoxicam absorption.Keywords: Analgesic, Microparticles, Controlled release, Lornoxicam, NSAIDs, Pharmacokinetic

Comparative Productive Performance of two Broiler Strains in Open Housing System

Background: The present study was conducted to compare the growth performance and ultimately to calculate the profitability of the two locally available commercial strains of broiler (Ross 308 and Cobb 500).Methods: For the purpose of study, 900 number of day-old chicks (DOC) of each strain were purchased from the local market. The birds were reared in conventional broiler house with the provision of standard managemental conditions throughout the experimental period. The parameters recorded on weekly basis were feed intake, body weight gain, feed conversion ratio (FCR) and mortality.Results: Result shown that the total body weight of Cobb-500 and Ross-308 on 1st week was 207.40±14 gram and 196.00±16 gram respectively and these result represented significant difference of weight gain (P0.05) among the strains. Furthermore, significant difference of feed conversion ratio (FCR) was observed (P<0.05) among both the strains but from day 7th till the market age weekly FCR of Cob-500 was significantly higher (P<0.05) than Ross-308. Comparatively high mortality (4.8±0.4%) was noticed in Ross broiler strain than Cobb broiler strain (3.7±0.4%). Conclusion: It was concluded from the current study that the Cobb-500 is performing better in conventional open housing system at high altitude than Ros-308.Keywords: Broiler; Cobb-500; Ross-308; Conventional broiler houses; Mortalit

Valuing the invaluable: park visitors' perceived importance and willingness to pay for urban park trees in Pakistan

In complex urban systems, urban parks are rare hot spots of nature, responsible for delivering a range of ecosystem services. Trees are critically important components of urban parks that provide many benefits, and, at the same time, face challenges such as lack of funds, mismanagement, climate change, pests, and diseases. There is a growing need to increase the urban tree cover to sustain urban ecosystems. Successful policymaking requires engagement with all stakeholders, especially park users. An understanding of how people's perceptions of the benefits and challenges faced by urban park trees is pivotal to making decisions that have long-standing support. We surveyed 521 park visitors in Multan, Pakistan, to assess their valuation of urban park trees, their understanding of the challenges faced by trees, and their willingness to pay (WTP) for increasing tree cover in urban parks. We found that people widely appreciated the ecosystem services provided by park trees. Provision of oxygen, shade, and clean air was considered the greatest benefits while cutting down, lack of space, and urbanization were perceived as the biggest challenges to trees. Respondents showed a WTP and believed in government–public cooperation for increasing tree cover. Income, age, and education were significant predictors of WTP. The study reflects findings in other development and geography contexts and highlights the need for broader dissemination of information on critical threats to park trees and underlines the potential for engaging locally to maximize effectively in the management of park trees

XMPP architecture and security challenges in an IoT ecosystem

The elusive quest for technological advancements with the aim to make human life easier has led to the development of the Internet of Things (IoT). IoT technology holds the potential to revolutionise our daily life, but not before overcoming barriers of security and data protection. IoTs’ steered a new era of free information that transformed life in ways that one could not imagine a decade ago. Hence, humans have started considering IoTs as a pervasive technology. This digital transformation does not stop here as the new wave of IoT is not about people, rather it is about intelligent connected devices. This proliferation of devices has also brought serious security issues not only to its users but the society as a whole. Application layer protocols form an integral component of IoT technology stack, and XMPP is one of such protocol that is efficient and reliable that allows real-time instant messaging mechanism in an IoT ecosystem. Though the XMPP specification possesses various security features, some vulnerabilities also exist that can be leveraged by the attacking entity to compromise an IoT network. This paper will present XMPP architecture along with various security challenges that exist in the protocol. The paper has also simulated a Denial of Service (DoS) attack on the XMPP server rendering its services unresponsive to its legitimate clients