2015 IEEE/ACM 4th International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering最新文献

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Mining Enterprise Models for Knowledgeable Decision Making 面向知识决策的矿业企业模型
Suman Roychoudhury, V. Kulkarni, Nikhil Bellarykar
{"title":"Mining Enterprise Models for Knowledgeable Decision Making","authors":"Suman Roychoudhury, V. Kulkarni, Nikhil Bellarykar","doi":"10.1109/RAISE.2015.8","DOIUrl":"https://doi.org/10.1109/RAISE.2015.8","url":null,"abstract":"Knowledge is stored in an enterprise in various forms ranging from unstructured operational data, legal documents to structured information like programs, as well as relational data stored in databases to semi-structured information stored in xml files. All these information if viewed from a holistic standpoint can help an enterprise to understand and reflect upon itself and thereby make knowledgeable decisions whenever required. In order to satisfy this objective of holistic knowledge representation and decision making, we begin with mining unstructured information present in an enterprise. In particular, in this paper, we intend to mine a document intensive business processes and extract information as a knowledge repository that captures the various stakeholders along with their intentions and the tasks they perform. The goal is to automate the validation of such business processes by eliminating any manual verification, which is time consuming and error prone. We believe this is the first step towards realizing our broader objective of collective modeling of enterprise knowledge that will involve mining of information available in unstructured, structured, relational as well as semi-structured form present in an enterprise.","PeriodicalId":402487,"journal":{"name":"2015 IEEE/ACM 4th International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering","volume":"114 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127175040","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}
引用次数: 4
Towards Interpretable Defect-Prone Component Analysis Using Genetic Fuzzy Systems 基于遗传模糊系统的可解释缺陷部件分析
Themistoklis G. Diamantopoulos, A. Symeonidis
{"title":"Towards Interpretable Defect-Prone Component Analysis Using Genetic Fuzzy Systems","authors":"Themistoklis G. Diamantopoulos, A. Symeonidis","doi":"10.1109/RAISE.2015.13","DOIUrl":"https://doi.org/10.1109/RAISE.2015.13","url":null,"abstract":"The problem of Software Reliability Prediction is attracting the attention of several researchers during the last few years. Various classification techniques are proposed in current literature which involve the use of metrics drawn from version control systems in order to classify software components as defect-prone or defect-free. In this paper, we create a novel genetic fuzzy rule-based system to efficiently model the defect-proneness of each component. The system uses a Mamdani-Assilian inference engine and models the problem as a one-class classification task. System rules are constructed using a genetic algorithm, where each chromosome represents a rule base (Pittsburgh approach). The parameters of our fuzzy system and the operators of the genetic algorithm are designed with regard to producing interpretable output. Thus, the output offers not only effective classification, but also a comprehensive set of rules that can be easily visualized to extract useful conclusions about the metrics of the software.","PeriodicalId":402487,"journal":{"name":"2015 IEEE/ACM 4th International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131470324","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}
引用次数: 5
Text Understanding for Programming in Natural Language: Control Structures 自然语言编程的文本理解:控制结构
Mathias Landhaeusser, Ronny Hug
{"title":"Text Understanding for Programming in Natural Language: Control Structures","authors":"Mathias Landhaeusser, Ronny Hug","doi":"10.1109/RAISE.2015.9","DOIUrl":"https://doi.org/10.1109/RAISE.2015.9","url":null,"abstract":"We investigate how natural languages such as English can be used as programming languages. Often, in natural language (as well as in programming) different actions happen at the same time or are repeated. In natural language we just say what is going to happen -- in programming we use control structures. Story lines without equivalent language/phrase structures are unnatural and programs without control structures are hard to read. An empirical study showed how users express control structures in natural language. We propose a new and automatic text analysis. It leverages Stanford's typed dependencies to detect sentence structures that imply strictly sequential control flows, repetition, and parallelism. The technique is analyzed in the context of Alice, a 3D programming environment, and Alice NLP, a system for programming Alice in ordinary English. We evaluate our approach with 52 texts with 795 control-flow-affected elements in total and show that 82% of these elements can be detected successfully. We performed a second evaluation with manually corrected input and find that our approach successfully detects the control structures 97% of the time in the absence of parser errors.","PeriodicalId":402487,"journal":{"name":"2015 IEEE/ACM 4th International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114670105","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}
引用次数: 11
Clustering Source Code Elements by Semantic Similarity Using Wikipedia 使用维基百科的语义相似度聚类源代码元素
Mirco Schindler, Oliver Fox, A. Rausch
{"title":"Clustering Source Code Elements by Semantic Similarity Using Wikipedia","authors":"Mirco Schindler, Oliver Fox, A. Rausch","doi":"10.1109/RAISE.2015.10","DOIUrl":"https://doi.org/10.1109/RAISE.2015.10","url":null,"abstract":"For humans it is no problem to determine if two words have a high or low semantic similarity in a given context. But is it possible to support a software developer or architect by using semantic data extracted from source code in the same way other relations like typical source code relations do? To answer this question we developed an approach to compute the semantic similarity by using Wikipedia as a textual corpus. In a case study we demonstrate this approach with a manageable software system. The results of using semantic similarities are compared with the outcome of using source code relations instead.","PeriodicalId":402487,"journal":{"name":"2015 IEEE/ACM 4th International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128677459","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}
引用次数: 12
Recommending Features and Feature Relationships from Requirements Documents for Software Product Lines 从软件产品线的需求文档中推荐特性和特性关系
Mostafa Hamza, R. Walker
{"title":"Recommending Features and Feature Relationships from Requirements Documents for Software Product Lines","authors":"Mostafa Hamza, R. Walker","doi":"10.1109/RAISE.2015.12","DOIUrl":"https://doi.org/10.1109/RAISE.2015.12","url":null,"abstract":"Feature models are a key element in software product lines, representing the supported features and their interrelationships within a family of software products. Recommendation systems for software engineering (RSSEs) are potentially useful in supporting the extraction, maintenance, and categorization of feature models. This paper focuses on the design and implementation of an RSSE to automatically recommend features for software product lines, the types of these features, and how they could be related to each other. Such a recommender should save time and tedium over doing the work manually. We present FFRE, a prototype recommendation tool for the extraction of features and their relationships from software requirements specification (SRS) documents. FFRE is based on natural language processing (NLP) techniques and heuristics. FFRE is evaluated qualitatively from four SRS documents and compared against other tools and approaches.","PeriodicalId":402487,"journal":{"name":"2015 IEEE/ACM 4th International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering","volume":"241 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124651220","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}
引用次数: 26
Black-Box Test Generation from Inferred Models 从推断模型生成黑盒测试
Petros Papadopoulos, Neil Walkinshaw
{"title":"Black-Box Test Generation from Inferred Models","authors":"Petros Papadopoulos, Neil Walkinshaw","doi":"10.1109/RAISE.2015.11","DOIUrl":"https://doi.org/10.1109/RAISE.2015.11","url":null,"abstract":"Automatically generating test inputs for components without source code (are 'black-box') and specification is challenging. One particularly interesting solution to this problem is to use Machine Learning algorithms to infer testable models from program executions in an iterative cycle. Although the idea has been around for over 30 years, there is little empirical information to inform the choice of suitable learning algorithms, or to show how good the resulting test sets are. This paper presents an openly available framework to facilitate experimentation in this area, and provides a proof-of-concept inference-driven testing framework, along with evidence of the efficacy of its test sets on three programs.","PeriodicalId":402487,"journal":{"name":"2015 IEEE/ACM 4th International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering","volume":"4 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116762732","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}
引用次数: 24
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