2021 36th IEEE/ACM International Conference on Automated Software Engineering Workshops (ASEW)最新文献

{"title":"Task Distribution and Human Resource Management Using Reinforcement Learning","authors":"C. Paduraru, Miruna Paduraru, C. Patilea","doi":"10.1109/ASEW52652.2021.00029","DOIUrl":"https://doi.org/10.1109/ASEW52652.2021.00029","url":null,"abstract":"The process of assigning tasks in large companies is a costly expenditure of human resources. Usually, many people are employed to distribute tasks as best as possible among the people involved in the projects. While there are software applications that support this effort, they are limited, and the people who make the decisions about where to send the various tasks considering load balancing, evaluating the capabilities of the possible solvers and many other factors are still handled manually. In this paper, we propose a solution using reinforcement learning to train an automatic agent capable of managing the process itself, thus reducing human effort and cost. Our method first attempts to learn from existing datasets and then improve itself in an unsupervised manner. The results are promising and validate our original idea that using an automated agent to address the observed gap can be a valuable addition to existing task management applications.","PeriodicalId":349977,"journal":{"name":"2021 36th IEEE/ACM International Conference on Automated Software Engineering Workshops (ASEW)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122162574","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PowDroid: Energy Profiling of Android Applications","authors":"Fares Bouaffar, Olivier Le Goaër, Adel Noureddine","doi":"10.1109/ASEW52652.2021.00055","DOIUrl":"https://doi.org/10.1109/ASEW52652.2021.00055","url":null,"abstract":"While the energy efficiency of mobile apps is receiving considerable attention in recent years, Android developers have little tools to assess the energy footprint of their applications. In this paper, we introduce PowDroid, our tool to estimate the energy consumption of Android application. It uses system-wide metrics and does not require access to applications’ source code. We run PowDroid on a use-case scenario comparing the energy footprint of applications in different categories.","PeriodicalId":349977,"journal":{"name":"2021 36th IEEE/ACM International Conference on Automated Software Engineering Workshops (ASEW)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128359892","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Decision-Making Biases and Cyber Attackers","authors":"Chelsea K. Johnson, R. Gutzwiller, Joseph Gervais, Kimberly J. Ferguson-Walter","doi":"10.1109/ASEW52652.2021.00038","DOIUrl":"https://doi.org/10.1109/ASEW52652.2021.00038","url":null,"abstract":"Cyber security is reliant on the actions of both machine and human and remains a domain of importance and continual evolution. While the study of human behavior has grown, less attention has been paid to the adversarial operator. Cyber environments consist of complex and dynamic situations where decisions are made with incomplete information. In such scenarios people form strategies based on simplified models of the world and are often efficient and effective, yet may result in judgement or decision-making bias. In this paper, we examine an initial list of biases affecting adversarial cyber actors. We use subject matter experts to derive examples and demonstrate these biases likely exist, and play a role in how attackers operate.","PeriodicalId":349977,"journal":{"name":"2021 36th IEEE/ACM International Conference on Automated Software Engineering Workshops (ASEW)","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134294430","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Investigation of Compound Variable Names Toward Automated Detection of Confusing Variable Pairs","authors":"Hirohisa Aman, S. Amasaki, Tomoyuki Yokogawa, Minoru Kawahara","doi":"10.1109/ASEW52652.2021.00036","DOIUrl":"https://doi.org/10.1109/ASEW52652.2021.00036","url":null,"abstract":"A successful naming of variables is key to making the source code readable. Programmers may use a compound variable name by concatenating two or more words to make it easier to understand and more informative. While each compound variable name itself may be easy-to-understand, a collection of such variables sometimes makes a “confusing” variable pair if their names are highly similar, e.g., “shippingHeight,” vs. “shippingWeight.” A confusing variable pair would adversely affect the code readability because it may cause a misreading or a mix-up of variables. Toward automated support for enhancing the code readability, this paper conducts a large-scale investigation of compound variable names in Java programs to find quantitative criteria of the confusing variable pairs. The investigation collects 31,806,749 pairs of compound-named variables from 684 open-source Java projects and analyzes them from two different perspectives of name similarity: the string similarity and the semantic similarity.","PeriodicalId":349977,"journal":{"name":"2021 36th IEEE/ACM International Conference on Automated Software Engineering Workshops (ASEW)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134452236","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Generating Context-Aware API Calls from Natural Language Description Using Neural Embeddings and Machine Translation","authors":"H. Phan, Arushi Sharma, A. Jannesari","doi":"10.1109/ASEW52652.2021.00050","DOIUrl":"https://doi.org/10.1109/ASEW52652.2021.00050","url":null,"abstract":"API calls can be described using natural language and can be implemented using a programming language. A programming environment that can process natural language descriptions within source code and provide context aware code suggestions can significantly improve productivity of developers and make programming more accessible to non-programmers and less experienced programmers. This paper proposes a context-aware tool called API Call Programming Interface (ACPI) which allows developers to write a natural language description for methods within source code and get correct and compilable API Call (AC) based on the text description and surrounding code. Existing work and code suggestion tools only consider the user's natural language description as input and ignore the contextual surrounding code. We take surrounding code into account and include information about local variable names within the code suggestion. Our approach consists of three modules. First, Method Name Generator, an unsupervised neural-embeddings-based algorithm to map the natural language description of methods to a list of most likely method names. Second, an AST Generator, a supervised Machine Translation model that predicts the structure of the AST from the list of the method names. And third, a Code Synthesizer that assigns local variables names to the AST to get the final method calls. Further, we include a Ranking Module that ranks the list of suggested method names based on their completeness. We evaluated our approach on data from 1000 high-quality Java projects and achieved an accuracy of 61% for API calls suggestions from natural language descriptions, which outperforms prior work and demonstrates the potential of our approach. We also conducted productivity experiments with 148 undergraduate participants to measure the usefulness of ACPI. The experiment showed that programming with ACPI can reduce programming time by 45% and increase programming accuracy from 19% to 83% when compared to programming without ACPI in four code completion tasks.","PeriodicalId":349977,"journal":{"name":"2021 36th IEEE/ACM International Conference on Automated Software Engineering Workshops (ASEW)","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134311425","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Message from the A-Mobile 2021 Chairs","authors":"","doi":"10.1109/asew52652.2021.00007","DOIUrl":"https://doi.org/10.1109/asew52652.2021.00007","url":null,"abstract":"","PeriodicalId":349977,"journal":{"name":"2021 36th IEEE/ACM International Conference on Automated Software Engineering Workshops (ASEW)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131086832","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Methodology for Human-Centred IoT Collectives Based on Socio-Ethical Policies","authors":"Amna Batool, S. Loke, Niroshinie Fernando, Jonathan Kua","doi":"10.1109/ASEW52652.2021.00042","DOIUrl":"https://doi.org/10.1109/ASEW52652.2021.00042","url":null,"abstract":"An emerging aspect to consider during human-device interaction is addressing ethical concerns about smart, internet-connected devices and making them behave in a more human-centred manner, i.e., ethically and in a socially acceptable manner. We propose that smart devices can be made to behave in a more human-centred manner, by the application of a set of policies to their fundamental operations, if a technique for defining their key roles and mapping ethical and administrative policies to them is developed. This paper proposes a methodology which consists of four primary phases, including concept development, defining and mapping policies, implementing the processing of policies and deploying the devices. Our suggested methodology may be used in a variety of situations where smart devices interact with people. For illustration, we have applied the proposed methodology to a supermarket scenario where each phase defined in the methodology has been followed.","PeriodicalId":349977,"journal":{"name":"2021 36th IEEE/ACM International Conference on Automated Software Engineering Workshops (ASEW)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126189294","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"JavaBERT: Training a Transformer-Based Model for the Java Programming Language","authors":"Nelson Tavares de Sousa, W. Hasselbring","doi":"10.1109/ASEW52652.2021.00028","DOIUrl":"https://doi.org/10.1109/ASEW52652.2021.00028","url":null,"abstract":"Code quality is and will be a crucial factor while developing new software code, requiring appropriate tools to ensure functional and reliable code. Machine learning techniques are still rarely used for software engineering tools, missing out the potential benefits of its application. Natural language processing has shown the potential to process text data regarding a variety of tasks. We argue, that such models can also show similar benefits for software code processing. In this paper, we investigate how models used for natural language processing can be trained upon software code. We introduce a data retrieval pipeline for software code and train a model upon Java software code. The resulting model, JavaBERT, shows a high accuracy on the masked language modeling task showing its potential for software engineering tools.","PeriodicalId":349977,"journal":{"name":"2021 36th IEEE/ACM International Conference on Automated Software Engineering Workshops (ASEW)","volume":"312 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122425726","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Readability and Understandability of Snippets Recommended by General-purpose Web Search Engines: A Comparative Study","authors":"C. Dantas, M. Maia","doi":"10.1109/ASEW52652.2021.00034","DOIUrl":"https://doi.org/10.1109/ASEW52652.2021.00034","url":null,"abstract":"Developers often search for reusable code snippets on general-purpose web search engines like Google, Yahoo! or Microsoft Bing. But some of these code snippets may have poor quality in terms of readability or understandability. In this paper, we propose an empirical analysis to analyze the readability and understandability score from snippets extracted from the web using three independent variables: ranking, general-purpose web search engine and recommended site. We collected the top-5 recommended sites and their respective code snippet recommendations using Google, Yahoo!, and Bing for 9,480 queries, and evaluate their readability and understandability scores. We found that some recommended sites have significantly better readability and understandability scores than others. The better-ranked code snippet is not necessarily more readable or understandable than a lower-ranked code snippet for all general-purpose web search engines. Moreover, considering the readability score, Google has better-ranked code snippets compared to Yahoo! or Microsoft Bing.","PeriodicalId":349977,"journal":{"name":"2021 36th IEEE/ACM International Conference on Automated Software Engineering Workshops (ASEW)","volume":"135 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117132874","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identifying non-natural language artifacts in bug reports","authors":"Thomas Hirsch, Birgit Hofer","doi":"10.1109/ASEW52652.2021.00046","DOIUrl":"https://doi.org/10.1109/ASEW52652.2021.00046","url":null,"abstract":"Bug reports are a popular target for natural language processing (NLP). However, bug reports often contain artifacts such as code snippets, log outputs and stack traces. These artifacts not only inflate the bug reports with noise, but often constitute a real problem for the NLP approach at hand and have to be removed. In this paper, we present a machine learning based approach to classify content into natural language and artifacts at line level implemented in Python. We show how data from GitHub issue trackers can be used for automated training set generation, and present a custom preprocessing approach for bug reports. Our model scores at 0.95 ROC-AUC and 0.93 F1 against our manually annotated validation set, and classifies 10k lines in 0.72 seconds. We cross evaluated our model against a foreign dataset and a foreign R model for the same task. The Python implementation of our model and our datasets are made publicly available under an open source license.","PeriodicalId":349977,"journal":{"name":"2021 36th IEEE/ACM International Conference on Automated Software Engineering Workshops (ASEW)","volume":"239 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123337168","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}