Jin Guo

In open-source software (OSS), the design of usability is often influenced by the discussions among community members on platforms such as i… (voir plus)ssue tracking systems (ITSs). However, digesting the rich information embedded in issue discussions can be a major challenge due to the vast number and diversity of the comments. We propose and evaluate ArguLens, a conceptual framework and automated technique leveraging an argumentation model to support effective understanding and consolidation of community opinions in ITSs. Through content analysis, we anatomized highly discussed usability issues from a large, active OSS project, into their argumentation components and standpoints. We then experimented with supervised machine learning techniques for automated argument extraction. Finally, through a study with experienced ITS users, we show that the information provided by ArguLens supported the digestion of usability-related opinions and facilitated the review of lengthy issues. ArguLens provides the direction of designing valuable tools for high-level reasoning and effective discussion about usability.

2020-04-23

Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (publié)

GitHub Repositories with Links to Academic Papers: Open Access, Traceability, and Evolution

Supatsara Wattanakriengkrai

Bodin Chinthanet

Hideaki Hata

Raula Gaikovina Kula

Christoph Treude

Ken-ichi Matsumoto

Traceability between published scientific breakthroughs and their implementation is essential, especially in the case of Open Source Softwar… (voir plus)e implements bleeding edge science into its code. However, aligning the link between GitHub repositories and academic papers can prove difficult, and the link impact remains unknown. This paper investigates the role of academic paper references contained in these repositories. We conducted a large-scale study of 20 thousand GitHub repositories to establish prevalence of references to academic papers. We use a mixed-methods approach to identify Open Access (OA), traceability and evolutionary aspects of the links. Although referencing a paper is not typical, we find that a vast majority of referenced academic papers are OA. In terms of traceability, our analysis revealed that machine learning is the most prevalent topic of repositories. These repositories tend to be affiliated with academic communities. More than half of the papers do not link back to any repository. A case study of referenced arXiv paper shows that most of these papers are high-impact and influential and do align with academia, referenced by repositories written in different programming languages. From the evolutionary aspect, we find very few changes of papers being referenced and links to them.

2020-04-01

arXiv.org (prépublication)

dblp.uni-trier.de

SENET: A Semantic Web for Supporting Automation of Software Engineering Tasks

Yalin Liu

Jinfeng Lin

Jane Cleland-Huang

Michael Vierhauser

Sugandha Lohar

The use of Natural Language (NL) interfaces to allow devices and applications to respond to verbal commands or free-form textual queries is … (voir plus)becoming increasingly prevalent in our society. To a large extent, their success in interpreting and responding to a request is dependent upon rich underlying ontologies and conceptual models that understand the technical or domain specific vocabulary of diverse users. The effective use of NL interfaces in the Software Engineering (SE) domains requires its own ontology models focusing upon software related terms and concepts. While many SE glossaries exist, they are often incomplete and tend to define the vocabulary for specific sub-fields without capturing associations between terms and phrases. This limits their usefulness for supporting NL-related tasks. In this paper we propose an approach for constructing and evolving a semantic network of software engineering concepts and phrases. Our approach starts with a set of existing SE glossaries, uses the existing glossary terms and explicitly defined associations as a starting point, uses machine learning-based techniques to dynamically identify and document additional associations between terms, leverages the network to interpret NL queries in the SE domain, and finally augments the resulting semantic network with feedback provided by users. We evaluate the viability of our approach within the sub-domain of Agile Software Development, focusing on requirements related queries, and show that the semantic network enhances the ability of an NL interface to correctly interpret and execute user queries.

2020-01-01

2020 IEEE Seventh International Workshop on Artificial Intelligence for Requirements Engineering (AIRE) (publié)

Towards Queryable and Traceable Domain Models

Rijul Saini

Gunter Mussbacher

Jörg Kienzle

Model-Driven Software Engineering encompasses various modelling formalisms for supporting software development. One such formalism is domain… (voir plus) modelling which bridges the gap between requirements expressed in natural language and analyzable and more concise domain models expressed in class diagrams. Due to the lack of modelling skills among novice modellers and time constraints in industrial projects, it is often not possible to build an accurate domain model manually. To address this challenge, we aim to develop an approach to extract domain models from problem descriptions written in natural language by combining rules based on natural language processing with machine learning. As a first step, we report on an automated and tool-supported approach with an accuracy of extracted domain models higher than existing approaches. In addition, the approach generates trace links for each model element of a domain model. The trace links enable novice modellers to execute queries on the extracted domain models to gain insights into the modelling decisions taken for improving their modelling skills. Furthermore, to evaluate our approach, we propose a novel comparison metric and discuss our experimental design. Finally, we present a research agenda detailing research directions and discuss corresponding challenges.

2020-01-01

2020 IEEE 28th International Requirements Engineering Conference (RE) (publié)

SST'19 - Software and Systems Traceability

Jan-Philipp Steghöfer

Nan Niu

Anas Mahmoud

Traceability is the ability to relate di erent artifacts during the development and operation of a system to each other. It enables program … (voir plus)comprehension, change impact analysis, and facilitates the cooperation of engineers from di erent disciplines. The 10th International Workshop on Software and Systems Traceability (former International Workshop on Traceability in Emerging Forms of Software Engineering, TEFSE), explored the role and impact of traceability in modern software and systems development. The event brought together researchers and practitioners to examine the challenges of recovering, maintaining, and utilizing traceability for the myriad forms of software and systems engineering artifacts. SST'19 was a highly interactive working event focused on discussing the main problems related to software traceability in particular in the context of opportunities and challenges posed by the recent progress in Arti cial Intelligence techniques and proposing possible solutions for such problems.

2019-11-14

ACM SIGSOFT Softw. Eng. Notes (publié)

Teaching Modelling Literacy: An Artificial Intelligence Approach

Rijul Saini

Gunter Mussbacher

Jörg Kienzle

In Model-Driven Engineering (MDE), models are used to build and analyze complex systems. In the last decades, different modelling formalisms… (voir plus) have been proposed for supporting software development. However, their adoption and practice strongly rely on mastering essential modelling skills to develop a complete and coherent model-based system. Moreover, it is often difficult for novice modellers to get direct and timely feedback and recommendations on their modelling strategies and decisions, particularly in large classroom settings which hinders their learning. Certainly, there is an opportunity to apply Artificial Intelligence (AI) techniques to an MDE learning environment to empower the provisioning of automated and intelligent modelling advocacy. In this paper, we propose a framework called ModBud (a modelling buddy) to educate novice modellers about the art of abstraction. ModBud uses natural language processing (NLP) and machine learning (ML) to create modelling bots with the aim of improving the modelling skills of novice modellers and assisting other practitioners, too. These bots could be used to support teaching with automatic creation or grading of models and enhance learning beyond the traditional classroom-based MDE education with timely feedback and personalized tutoring. Research challenges for the proposed framework are discussed and a research roadmap is presented.

2019-09-01

2019 ACM/IEEE 22nd International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C) (publié)

Activity-Based Analysis of Open Source Software Contributors: Roles and Dynamics

Jinghui Cheng

Contributors to open source software (OSS) communities assume diverse roles to take different responsibilities. One major limitation of the … (voir plus)current OSS tools and platforms is that they provide a uniform user interface regardless of the activities performed by the various types of contributors. This paper serves as a non-trivial first step towards resolving this challenge by demonstrating a methodology and establishing knowledge to understand how the contributors' roles and their dynamics, reflected in the activities contributors perform, are exhibited in OSS communities. Based on an analysis of user action data from 29 GitHub projects, we extracted six activities that distinguished four Active roles and five Supporting roles of OSS contributors, as well as patterns in role changes. Through the lens of the Activity Theory, these findings provided rich design guidelines for OSS tools to support diverse contributor roles.

2019-05-27

2019 IEEE/ACM 12th International Workshop on Cooperative and Human Aspects of Software Engineering (CHASE) (publié)

Analysis and Detection of Information Types of Open Source Software Issue Discussions

Deeksha M. Arya

Wenting Wang

Jinghui Cheng

Most modern Issue Tracking Systems (ITSs) for open source software (OSS) projects allow users to add comments to issues. Over time, these co… (voir plus)mments accumulate into discussion threads embedded with rich information about the software project, which can potentially satisfy the diverse needs of OSS stakeholders. However, discovering and retrieving relevant information from the discussion threads is a challenging task, especially when the discussions are lengthy and the number of issues in ITSs are vast. In this paper, we address this challenge by identifying the information types presented in OSS issue discussions. Through qualitative content analysis of 15 complex issue threads across three projects hosted on GitHub, we uncovered 16 information types and created a labeled corpus containing 4656 sentences. Our investigation of supervised, automated classification techniques indicated that, when prior knowledge about the issue is available, Random Forest can effectively detect most sentence types using conversational features such as the sentence length and its position. When classifying sentences from new issues, Logistic Regression can yield satisfactory performance using textual features for certain information types, while falling short on others. Our work represents a nontrivial first step towards tools and techniques for identifying and obtaining the rich information recorded in the ITSs to support various software engineering activities and to satisfy the diverse needs of OSS stakeholders.

2019-05-25

2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE) (publié)

Usability of Virtual Reality Application Through the Lens of the User Community: A Case Study

Wenting Wang

Jinghui Cheng

The increasing availability and diversity of virtual reality (VR) applications highlighted the importance of their usability. Function-orien… (voir plus)ted VR applications posed new challenges that are not well studied in the literature. Moreover, user feedback becomes readily available thanks to modern software engineering tools, such as app stores and open source platforms. Using Firefox Reality as a case study, we explored the major types of VR usability issues raised in these platforms. We found that 77% of usability feedbacks can be mapped to Nielsen's heuristics while few were mappable to VR-specific heuristics. This result indicates that Nielsen's heuristics could potentially help developers address the usability of this VR application in its early development stage. This work paves the road for exploring tools leveraging the community effort to promote the usability of function-oriented VR applications.

2019-05-02

Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems (publié)

Toward Requirements Specification for Machine-Learned Components

Mona Rahimi

Sahar Kokaly

Marsha Chechik

In current practice, the behavior of Machine-Learned Components (MLCs) is not sufficiently specified by the predefined requirements. Instead… (voir plus), they "learn" existing patterns from the available training data, and make predictions for unseen data when deployed. On the surface, their ability to extract patterns and to behave accordingly is specifically useful for hard-to-specify concepts in certain safety critical domains (e.g., the definition of a pedestrian in a pedestrian detection component in a vehicle). However, the lack of requirements specifications on their behaviors makes further software engineering tasks challenging for such components. This is especially concerning for tasks such as safety assessment and assurance. In this position paper, we call for more attention from the requirements engineering community on supporting the specification of requirements for MLCs in safety critical domains. Towards that end, we propose an approach to improve the process of requirements specification in which an MLC is developed and operates by explicitly specifying domain-related concepts. Our approach extracts a universally accepted benchmark for hard-to-specify concepts (e.g., "pedestrian") and can be used to identify gaps in the associated dataset and the constructed machine-learned model.

2019-01-01

2019 IEEE 27th International Requirements Engineering Conference Workshops (REW) (publié)

Domain Knowledge Discovery Guided by Software Trace Links

Natawut Monaikul

Jane Cleland-Huang

Software-intensive projects are specified and modeled using domain terminology. Knowledge of the domain terminology is necessary for perform… (voir plus)ing many Software Engineering tasks such as impact analysis, compliance verification, and safety certification. However, discovering domain terminology and reasoning about their interrelationships for highly technical software and system engineering domains is a complex task which requires significant domain expertise and human effort. In this paper, we present a novel approach for leveraging trace links in software intensive systems to guide the process of mining facts that contain domain knowledge. The trace links which drive our mining process, define relationships between artifacts such as regulations and requirements and enable a guided search through high-yield combinations of domain terms. Our proof-of-concept evaluation shows that our approach aids in the discovery of domain facts even in highly complex technical domains. These domain facts can provide support for a variety of Software Engineering activities. As a use case, we demonstrate how the mined facts can facilitate the task of project Q&A.

2018-08-21

2018 5th International Workshop on Artificial Intelligence for Requirements Engineering (AIRE) (publié)

Traceability in the Wild: Automatically Augmenting Incomplete Trace Links

Michael Rath

Jacob Rendall

Jane Cleland-Huang

Patrick Mäder

Software and systems traceability is widely accepted as an essential element for supporting many software development tasks. Today's version… (voir plus) control systems provide inbuilt features that allow developers to tag each commit with one or more issue ID, thereby providing the building blocks from which project-wide traceability can be established between feature requests, bug fixes, commits, source code, and specific developers. However, our analysis of six open source projects showed that on average only 60% of the commits were linked to specific issues. Without these fundamental links the entire set of project-wide links will be incomplete, and therefore not trustworthy. In this paper we address the fundamental problem of missing links between commits and issues. Our approach leverages a combination of process and text-related features characterizing issues and code changes to train a classifier to identify missing issue tags in commit messages, thereby generating the missing links. We conducted a series of experiments to evaluate our approach against six open source projects and showed that it was able to effectively recommend links for tagging issues at an average of 96% recall and 33% precision. In a related task for augmenting a set of existing trace links, the classifier returned precision at levels greater than 89% in all projects and recall of 50%.

2018-05-27

Proceedings of the 40th International Conference on Software Engineering (publié)