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

Most modern Issue Tracking Systems (ITSs) for open source software (OSS) projects allow users to add comments to issues. Over time, these comments 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.Comment: 41st ACM/IEEE International Conference on Software Engineering (ICSE2019

Bichromatic field generation from double-four-wave mixing in a double-electromagnetically induced transparency system

We demonstrate the double electromagnetically induced transparency (double-EIT) and double four-wave mixing (double-FWM) based on a new scheme of non-degenerate four-wave mixing (FWM) involving five levels of a cold 85Rb atomic ensemble, in which the double-EIT windows are used to transmit the probe field and enhance the third-order nonlinear susceptibility. The phase-matching conditions for both four-wave mixings could be satisfied simultaneously. The frequency of one component of the generated bichromatic field is less than the other by the ground-state hyperfine splitting (3GHz). This specially designed experimental scheme for simultaneously generating different nonlinear wave-mixing processes is expected to find applications in quantum information processing and cross phase modulation. Our results agree well with the theoretical simulation.Comment: Accepted by NJ

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

Contributors to open source software (OSS) communities assume diverse roles to take different responsibilities. One major limitation of the 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.Comment: 12th International Workshop on Cooperative and Human Aspects of Software Engineering (CHASE 2019

Semantically Enhanced Software Traceability Using Deep Learning Techniques

In most safety-critical domains the need for traceability is prescribed by certifying bodies. Trace links are generally created among requirements, design, source code, test cases and other artifacts, however, creating such links manually is time consuming and error prone. Automated solutions use information retrieval and machine learning techniques to generate trace links, however, current techniques fail to understand semantics of the software artifacts or to integrate domain knowledge into the tracing process and therefore tend to deliver imprecise and inaccurate results. In this paper, we present a solution that uses deep learning to incorporate requirements artifact semantics and domain knowledge into the tracing solution. We propose a tracing network architecture that utilizes Word Embedding and Recurrent Neural Network (RNN) models to generate trace links. Word embedding learns word vectors that represent knowledge of the domain corpus and RNN uses these word vectors to learn the sentence semantics of requirements artifacts. We trained 360 different configurations of the tracing network using existing trace links in the Positive Train Control domain and identified the Bidirectional Gated Recurrent Unit (BI-GRU) as the best model for the tracing task. BI-GRU significantly out-performed state-of-the-art tracing methods including the Vector Space Model and Latent Semantic Indexing.Comment: 2017 IEEE/ACM 39th International Conference on Software Engineering (ICSE

EARLY PRECAMBRIAN CRUSTAL EVOLUTION OF THE BELOMORIAN AND TRANS-NORTH CHINA OROGENS AND SUPERCONTINENTS RECONSTRUCTION

Comparative analysis of the crustal evolution of the Early Precambrian Belomorian and Trans-North China orogens (Fig. 1) has shown [Slabunov et al., 2015] that: Both belts were formed by the superposition of two Precambrian orogenies. The earth crust of the Belomorian belt was produced during the Mesoarchaean to Neoarchaean Belomorian collisional orogeny [Slabunov, 2008; Slabunov et al., 2006] and then was reworked during the Palaeoproterozoic Lapland-Kola collisional orogeny [Daly at al., 2006; Balagansky et al., 2014]. The earth crust of the Trans-North China orogen was formed during a Neoarchean accretionary orogeny and then was reworked during a Paleoproterozoic collisional orogeny [Zhao et al., 2012; Guo et al., 2012, 2005]. The Lapland granulite belt is the core of the Lapland-Kola Palaeoproterozoic collisional orogen in the Fennoscandian shield and the Khondolite belt occupies the same tectonic position in a Palaeoproterozoic collisional orogen in the North China craton.Comparative analysis of the crustal evolution of the Early Precambrian Belomorian and Trans-North China orogens (Fig. 1) has shown [Slabunov et al., 2015] that: Both belts were formed by the superposition of two Precambrian orogenies. The earth crust of the Belomorian belt was produced during the Mesoarchaean to Neoarchaean Belomorian collisional orogeny [Slabunov, 2008; Slabunov et al., 2006] and then was reworked during the Palaeoproterozoic Lapland-Kola collisional orogeny [Daly at al., 2006; Balagansky et al., 2014]. The earth crust of the Trans-North China orogen was formed during a Neoarchean accretionary orogeny and then was reworked during a Paleoproterozoic collisional orogeny [Zhao et al., 2012; Guo et al., 2012, 2005]. The Lapland granulite belt is the core of the Lapland-Kola Palaeoproterozoic collisional orogen in the Fennoscandian shield and the Khondolite belt occupies the same tectonic position in a Palaeoproterozoic collisional orogen in the North China craton

GUILGET: GUI Layout GEneration with Transformer

Sketching out Graphical User Interface (GUI) layout is part of the pipeline of designing a GUI and a crucial task for the success of a software application. Arranging all components inside a GUI layout manually is a time-consuming task. In order to assist designers, we developed a method named GUILGET to automatically generate GUI layouts from positional constraints represented as GUI arrangement graphs (GUI-AGs). The goal is to support the initial step of GUI design by producing realistic and diverse GUI layouts. The existing image layout generation techniques often cannot incorporate GUI design constraints. Thus, GUILGET needs to adapt existing techniques to generate GUI layouts that obey to constraints specific to GUI designs. GUILGET is based on transformers in order to capture the semantic in relationships between elements from GUI-AG. Moreover, the model learns constraints through the minimization of losses responsible for placing each component inside its parent layout, for not letting components overlap if they are inside the same parent, and for component alignment. Our experiments, which are conducted on the CLAY dataset, reveal that our model has the best understanding of relationships from GUI-AG and has the best performances in most of evaluation metrics. Therefore, our work contributes to improved GUI layout generation by proposing a novel method that effectively accounts for the constraints on GUI elements and paves the road for a more efficient GUI design pipeline.Comment: 12 pages, 5 figures, Canadian AI Conference 202