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preprint2020arXiv

A Context Aware Framework for IoT Based Healthcare Monitoring Systems

This paper introduces an investigation of the healthcare monitoring systems and their provisioning in the IoT platform. The different roles that exist in healthcare systems are specified and modeled here. This paper also attempts to introduce and propose a generic framework for the design and development of context aware healthcare monitoring systems in the IoT platform. In such a framework, the fundamental components of the healthcare monitoring systems are identified and modelled as well as the relationship between these components. The paper also stresses on the crucial role played by the AI field in addressing resilient context aware healthcare monitoring systems. Architecturally, this framework is based on a distributed layered architecture where the different components are deployed over the physical layer, fog platform and the cloud platform.

preprint2020arXiv

A systematic mapping study of developer social network research

Developer social networks (DSNs) are a tool for the analysis of community structures and collaborations between developers in software projects and software ecosystems. Within this paper, we present the results of a systematic mapping study on the use of DSNs in software engineering research. We identified 255 primary studies on DSNs. We mapped the primary studies to research directions, collected information about the data sources and the size of the studies, and conducted a bibliometric assessment. We found that nearly half of the research investigates the structure of developer communities. Other frequent topics are prediction systems build using DSNs, collaboration behavior between developers, and the roles of developers. Moreover, we determined that many publications use a small sample size regarding the number of projects, which could be problematic for the external validity of the research. Our study uncovered several open issues in the state of the art, e.g., studying inter-company collaborations, using multiple information sources for DSN research, as well as general lack of reporting guidelines or replication studies.

preprint2020arXiv

OCoR: An Overlapping-Aware Code Retriever

Code retrieval helps developers reuse the code snippet in the open-source projects. Given a natural language description, code retrieval aims to search for the most relevant code among a set of code. Existing state-of-the-art approaches apply neural networks to code retrieval. However, these approaches still fail to capture an important feature: overlaps. The overlaps between different names used by different people indicate that two different names may be potentially related (e.g., "message" and "msg"), and the overlaps between identifiers in code and words in natural language descriptions indicate that the code snippet and the description may potentially be related. To address these problems, we propose a novel neural architecture named OCoR, where we introduce two specifically-designed components to capture overlaps: the first embeds identifiers by character to capture the overlaps between identifiers, and the second introduces a novel overlap matrix to represent the degrees of overlaps between each natural language word and each identifier. The evaluation was conducted on two established datasets. The experimental results show that OCoR significantly outperforms

preprint2020arXiv

An Empirical Study of In-App Advertising Issues Based on Large Scale App Review Analysis

In-app advertising closely relates to app revenue. Reckless ad integration could adversely impact app reliability and user experience, leading to loss of income. It is very challenging to balance the ad revenue and user experience for app developers. In this paper, we present a large-scale analysis on ad-related user feedback. The large user feedback data from App Store and Google Play allow us to summarize ad-related app issues comprehensively and thus provide practical ad integration strategies for developers. We first define common ad issues by manually labeling a statistically representative sample of ad-related feedback, and then build an automatic classifier to categorize ad-related feedback. We study the relations between different ad issues and user ratings to identify the ad issues poorly scored by users. We also explore the fix durations of ad issues across platforms for extracting insights into prioritizing ad issues for ad maintenance. We summarize 15 types of ad issues by manually annotating 903/36,309 ad-related user reviews. From a statistical analysis of 36,309 ad-related reviews, we find that users care most about the number of unique ads and ad display frequency d

preprint2020arXiv

Skyline: Interactive In-Editor Computational Performance Profiling for Deep Neural Network Training

Training a state-of-the-art deep neural network (DNN) is a computationally-expensive and time-consuming process, which incentivizes deep learning developers to debug their DNNs for computational performance. However, effectively performing this debugging requires intimate knowledge about the underlying software and hardware systems---something that the typical deep learning developer may not have. To help bridge this gap, we present Skyline: a new interactive tool for DNN training that supports in-editor computational performance profiling, visualization, and debugging. Skyline's key contribution is that it leverages special computational properties of DNN training to provide (i) interactive performance predictions and visualizations, and (ii) directly manipulatable visualizations that, when dragged, mutate the batch size in the code. As an in-editor tool, Skyline allows users to leverage these diagnostic features to debug the performance of their DNNs during development. An exploratory qualitative user study of Skyline produced promising results; all the participants found Skyline to be useful and easy to use.

preprint2020arXiv

Emerging App Issue Identification via Online Joint Sentiment-Topic Tracing

Millions of mobile apps are available in app stores, such as Apple's App Store and Google Play. For a mobile app, it would be increasingly challenging to stand out from the enormous competitors and become prevalent among users. Good user experience and well-designed functionalities are the keys to a successful app. To achieve this, popular apps usually schedule their updates frequently. If we can capture the critical app issues faced by users in a timely and accurate manner, developers can make timely updates, and good user experience can be ensured. There exist prior studies on analyzing reviews for detecting emerging app issues. These studies are usually based on topic modeling or clustering techniques. However, the short-length characteristics and sentiment of user reviews have not been considered. In this paper, we propose a novel emerging issue detection approach named MERIT to take into consideration the two aforementioned characteristics. Specifically, we propose an Adaptive Online Biterm Sentiment-Topic (AOBST) model for jointly modeling topics and corresponding sentiments that takes into consideration app versions. Based on the AOBST model, we infer the topics negative

preprint2020arXiv

Model-based Automated Testing of Mobile Applications: An Industrial Case Study

Automatic testing of mobile applications has been a well-researched area in recent years. However, testing in industry is still a very manual practice, as research results have not been fully transferred and adopted. Considering mobile applications, manual testing has the additional burden of adequate testing posed by a large number of available devices and different configurations, as well as the maintenance and setup of such devices. In this paper, we propose and evaluate the use of a model-based test generation approach, where generated tests are executed on a set of cloud-hosted real mobile devices. By using a model-based approach we generate dynamic, less brittle, and implementation simple test cases. The test execution on multiple real devices with different configurations increase the confidence in the implementation of the system under test. Our evaluation shows that the used approach produces a high coverage of the parts of the application related to user interactions. Nevertheless, the inclusion of external services in test generation is required in order to additionally increase the coverage of the complete application. Furthermore, we present the lessons learned while t

preprint2020arXiv

DApp for Rating

Lots of existing web applications include a component for rating internet resources (e.g., social media platforms include mechanisms for rating videos or posts). Based on the obtained rating, the most popular internet resources can generate large amounts of money from advertising. One issue here is that the existing rating systems resources are entirely controlled by a single entity (e.g., social media platforms). In this paper we present a blockchain-based decentralized application for rating internet resources. The proposed solution provides a transparent rating mechanism, since no central authority is involved and the rating operations are handled by a specialised smart contract. We provide an implementation of our idea, where we combine existing authentication methods with blockchain specific features so that anonymity is preserved. We show that this approach is better than existing rating components present in various web applications.

preprint2020arXiv

Failures and Fixes: A Study of Software System Incident Response

This paper presents the results of a research study related to software system failures, with the goal of understanding how we might better evolve, maintain and support software systems in production. We have qualitatively analyzed thirty incidents: fifteen collected through in depth interviews with engineers, and fifteen sampled from publicly published incident reports (generally produced as part of postmortem reviews). Our analysis focused on understanding and categorizing how failures occurred, and how they were detected, investigated and mitigated. We also captured analytic insights related to the current state of the practice and associated challenges in the form of 11 key observations. For example, we observed that failures can cascade through a system leading to major outages; and that often engineers do not understand the scaling limits of systems they are supporting until those limits are exceeded. We argue that the challenges we have identified can lead to improvements to how systems are engineered and supported.

preprint2020arXiv

Hybrid Deep Neural Networks to Infer State Models of Black-Box Systems

Inferring behavior model of a running software system is quite useful for several automated software engineering tasks, such as program comprehension, anomaly detection, and testing. Most existing dynamic model inference techniques are white-box, i.e., they require source code to be instrumented to get run-time traces. However, in many systems, instrumenting the entire source code is not possible (e.g., when using black-box third-party libraries) or might be very costly. Unfortunately, most black-box techniques that detect states over time are either univariate, or make assumptions on the data distribution, or have limited power for learning over a long period of past behavior. To overcome the above issues, in this paper, we propose a hybrid deep neural network that accepts as input a set of time series, one per input/output signal of the system, and applies a set of convolutional and recurrent layers to learn the non-linear correlations between signals and the patterns, over time. We have applied our approach on a real UAV auto-pilot solution from our industry partner with half a million lines of C code. We ran 888 random recent system-level test cases and inferred states, over ti

preprint2020arXiv

MAR: A structure-based search engine for models

The availability of shared software models provides opportunities for reusing, adapting and learning from them. Public models are typically stored in a variety of locations, including model repositories, regular source code repositories, web pages, etc. To profit from them developers need effective search mechanisms to locate the models relevant for their tasks. However, to date, there has been little success in creating a generic and efficient search engine specially tailored to the modelling domain. In this paper we present MAR, a search engine for models. MAR is generic in the sense that it can index any type of model if its meta-model is known. MAR uses a query-by-example approach, that is, it uses example models as queries. The search takes the model structure into account using the notion of bag of paths, which encodes the structure of a model using paths between model elements and is a representation amenable for indexing. MAR is built over HBase using a specific design to deal with large repositories. Our benchmarks show that the engine is efficient and has fast response times in most cases. We have also evaluated the precision of the search engine by creating model mutants

preprint2020arXiv

Towards Guidelines for Assessing Qualities of Machine Learning Systems

Nowadays, systems containing components based on machine learning (ML) methods are becoming more widespread. In order to ensure the intended behavior of a software system, there are standards that define necessary quality aspects of the system and its components (such as ISO/IEC 25010). Due to the different nature of ML, we have to adjust quality aspects or add additional ones (such as trustworthiness) and be very precise about which aspect is really relevant for which object of interest (such as completeness of training data), and how to objectively assess adherence to quality requirements. In this article, we present the construction of a quality model (i.e., evaluation objects, quality aspects, and metrics) for an ML system based on an industrial use case. This quality model enables practitioners to specify and assess quality requirements for such kinds of ML systems objectively. In the future, we want to learn how the term quality differs between different types of ML systems and come up with general guidelines for specifying and assessing qualities of ML systems.

preprint2020arXiv

Leading DevOps Practice and Principle Adoption

This research, undertaken in highly structured software-intensive organizations, outlines challenges associated to agile, lean and DevOps practices and principles adoption. The approach collected data via a series of thirty (30) interviews, with practitioners from the EMEA region (Czech Republic, Estonia, Italy, Georgia, Greece, The Netherlands, Saudi Arabia, South Africa, UAE, UK), working in nine (9) different industry domains and ten (10) different countries. A set of agile, lean and DevOps practices and principles, which organizations choose to include in their DevOps adoption journeys were identified. The most frequently adopted structured service management practices, contributing to DevOps practice adoption success, indicate that those with software development and operation roles in DevOps-oriented organizations benefit from existence of highly structured service management approaches such as ITIL.

preprint2020arXiv

Software Effort Estimation using parameter tuned Models

Software estimation is one of the most important activities in the software project. The software effort estimation is required in the early stages of software life cycle. Project Failure is the major problem undergoing nowadays as seen by software project managers. The imprecision of the estimation is the reason for this problem. Assize of software size grows, it also makes a system complex, thus difficult to accurately predict the cost of software development process. The greatest pitfall of the software industry was the fast-changing nature of software development which has made it difficult to develop parametric models that yield high accuracy for software development in all domains. We need the development of useful models that accurately predict the cost of developing a software product. This study presents the novel analysis of various regression models with hyperparameter tuning to get the effective model. Nine different regression techniques are considered for model development

preprint2020arXiv

PLIERS: A Process that Integrates User-Centered Methods into Programming Language Design

Programming language design requires making many usability-related design decisions. However, existing HCI methods can be impractical to apply to programming languages: they have high iteration costs, programmers require significant learning time, and user performance has high variance. To address these problems, we adapted both formative and summative HCI methods to make them more suitable for programming language design. We integrated these methods into a new process, PLIERS, for designing programming languages in a user-centered way. We evaluated PLIERS by using it to design two new programming languages. Glacier extends Java to enable programmers to express immutability properties effectively and easily. Obsidian is a language for blockchains that includes verification of critical safety properties. Summative usability studies showed that programmers were able to program effectively in both languages after short training periods.

preprint2020arXiv

Path homology as a stronger analogue of cyclomatic complexity

Cyclomatic complexity is an incompletely specified but mathematically principled software metric that can be usefully applied to both source and binary code. We consider the application of path homology as a stronger analogue of cyclomatic complexity. We have implemented an algorithm to compute path homology in arbitrary dimension and applied it to several classes of relevant flow graphs, including randomly generated flow graphs representing structured and unstructured control flow. We also compared path homology and cyclomatic complexity on a set of disassembled binaries obtained from the grep utility. There exist control flow graphs realizable at the assembly level with nontrivial path homology in arbitrary dimension. We exhibit several classes of examples in this vein while also experimentally demonstrating that path homology gives identicial results to cyclomatic complexity for at least one detailed notion of structured control flow. We also experimentally demonstrate that the two notions differ on disassembled binaries, and we highlight an example of extreme disagreement. Path homology empirically generalizes cyclomatic complexity for an elementary notion of structured code an

preprint2020arXiv

Researcher Bias in Software Engineering Experiments: a Qualitative Investigation

Researcher Bias (RB) occurs when researchers influence the results of an empirical study based on their expectations.RB might be due to the use of Questionable Research Practices(QRPs). In research fields like medicine, blinding techniques have been applied to counteract RB. We conducted an explorative qualitative survey to investigate RB in Software Engineering (SE)experiments, with respect to (i) QRPs potentially leading to RB, (ii) causes behind RB, and (iii) possible actions to counteract including blinding techniques. Data collection was based on semi-structured interviews. We interviewed nine active experts in the empirical SE community. We then analyzed the transcripts of these interviews through thematic analysis. We found that some QRPs are acceptable in certain cases. Also, it appears that the presence of RB is perceived in SE and, to counteract RB, a number of solutions have been highlighted: some are intended for SE researchers and others for the boards of SE research outlets.

preprint2020arXiv

Identifying candidate routines for Robotic Process Automation from unsegmented UI logs

Robotic Process Automation (RPA) is a technology to develop software bots that automate repetitive sequences of interactions between users and software applications (a.k.a. routines). To take full advantage of this technology, organizations need to identify and to scope their routines. This is a challenging endeavor in large organizations, as routines are usually not concentrated in a handful of processes, but rather scattered across the process landscape. Accordingly, the identification of routines from User Interaction (UI) logs has received significant attention. Existing approaches to this problem assume that the UI log is segmented, meaning that it consists of traces of a task that is presupposed to contain one or more routines. However, a UI log usually takes the form of a single unsegmented sequence of events. This paper presents an approach to discover candidate routines from unsegmented UI logs in the presence of noise, i.e. events within or between routine instances that do not belong to any routine. The approach is implemented as an open-source tool and evaluated using synthetic and real-life UI logs.

preprint2020arXiv

M3: Semantic API Migrations

Library migration is a challenging problem, where most existing approaches rely on prior knowledge. This can be, for example, information derived from changelogs or statistical models of API usage. This paper addresses a different API migration scenario where there is no prior knowledge of the target library. We have no historical changelogs and no access to its internal representation. To tackle this problem, this paper proposes a novel approach (M$^3$), where probabilistic program synthesis is used to semantically model the behavior of library functions. Then, we use an SMT-based code search engine to discover similar code in user applications. These discovered instances provide potential locations for API migrations. We evaluate our approach against 7 well-known libraries from varied application domains, learning correct implementations for 94 functions. Our approach is integrated with standard compiler tooling, and we use this integration to evaluate migration opportunities in 9 existing C/C++ applications with over 1MLoC. We discover over 7,000 instances of these functions, of which more than 2,000 represent migration opportunities.

preprint2020arXiv

Basis to develop a platform for multiple-scale complex systems modeling and visualization: MoNet

This work presents some characteristics of MoNet, a digital platform for the modeling and visualization of complex systems. Emphasis is on the ideas that allowed the successful progressive development of this modeling platform, which goes along with the implementation of applications to the modeling of several studied systems. The platform can represent different aspects of systems modeled at different observation scales. This tool offers advantages in the sense of favoring the perception of the phenomenon of the emergence of information, associated with changes of scale. This paper also includes some criteria used for the construction of this modeling platform. The power of current computers has made practical representing graphic resources such as shapes, line thickness, overlaying-text tags, colors, and transparencies, in the graphical modeling of systems. By visualizing diagrams conveniently designed to highlight contrasts, these modeling platforms allow the recognition of patterns that drive our understanding of systems and their structure. Graphs reflecting the benefits of the tool regarding the visualization of systems at different scales of observation are presented to illu

preprint2020arXiv

A First Look at the Deprecation of RESTful APIs: An Empirical Study

REpresentational State Transfer (REST) is considered as one standard software architectural style to build web APIs that can integrate software systems over the internet. However, while connecting systems, RESTful APIs might also break the dependent applications that rely on their services when they introduce breaking changes, e.g., an older version of the API is no longer supported. To warn developers promptly and thus prevent critical impact on downstream applications, a deprecated-removed model should be followed, and deprecation-related information such as alternative approaches should also be listed. While API deprecation analysis as a theme is not new, most existing work focuses on non-web APIs, such as the ones provided by Java and Android. To investigate RESTful API deprecation, we propose a framework called RADA (RESTful API Deprecation Analyzer). RADA is capable of automatically identifying deprecated API elements and analyzing impacted operations from an OpenAPI specification, a machine-readable profile for describing RESTful web service. We apply RADA on 2,224 OpenAPI specifications of 1,368 RESTful APIs collected from APIs.guru, the largest directory of OpenAPI specifi

preprint2020arXiv

Neural Code Search Revisited: Enhancing Code Snippet Retrieval through Natural Language Intent

In this work, we propose and study annotated code search: the retrieval of code snippets paired with brief descriptions of their intent using natural language queries. On three benchmark datasets, we investigate how code retrieval systems can be improved by leveraging descriptions to better capture the intents of code snippets. Building on recent progress in transfer learning and natural language processing, we create a domain-specific retrieval model for code annotated with a natural language description. We find that our model yields significantly more relevant search results (with absolute gains up to 20.6% in mean reciprocal rank) compared to state-of-the-art code retrieval methods that do not use descriptions but attempt to compute the intent of snippets solely from unannotated code.

preprint2020arXiv

Characterizing Erasable Accounts in Ethereum

Being the most popular permissionless blockchain that supports smart contracts, Ethereum allows any user to create accounts on it. However, not all accounts matter. For example, the accounts due to attacks can be removed. In this paper, we conduct the first investigation on erasable accounts that can be removed to save system resources and even users' money (i.e., ETH or gas). In particular, we propose and develop a novel tool named GLASER, which analyzes the State DataBase of Ethereum to discover five kinds of erasable accounts. The experimental results show that GLASER can accurately reveal 508,482 erasable accounts and these accounts lead to users wasting more than 106 million dollars. GLASER can help stop further economic loss caused by these detected accounts. Moreover, GLASER characterizes the attacks/behaviors related to detected erasable accounts through graph analysis.

preprint2020arXiv

A3Ident: A Two-phased Approach to Identify the Leading Authors of Android Apps

Authorship identification is the process of identifying and classifying authors through given codes. Authorship identification can be used in a wide range of software domains, e.g., code authorship disputes, plagiarism detection, exposure of attackers' identity. Besides the inherent challenges from legacy software development, framework programming and crowdsourcing mode in Android raise the difficulties of authorship identification significantly. More specifically, widespread third party libraries and inherited components (e.g., classes, methods, and variables) dilute the primary code within the entire Android app and blur the boundaries of code written by different authors. However, prior research has not well addressed these challenges. To this end, we design a two-phased approach to attribute the primary code of an Android app to the specific developer. In the first phase, we put forward three types of strategies to identify the relationships between Java packages in an app, which consist of context, semantic and structural relationships. A package aggregation algorithm is developed to cluster all packages that are of high probability written by the same authors. In the sec