Human-like summaries from heterogeneous and time-windowed software development artefacts

First Online: 02 September 2020Automatic text summarisation has drawn considerable interest in the area of software engineering. It is challenging to summarise the activities related to a software project, (1) because of the volume and heterogeneity of involved software artefacts, and (2) because it is unclear what information a developer seeks in such a multi-document summary. We present the first framework for summarising multi-document software artefacts containing heterogeneous data within a given time frame. To produce human-like summaries, we employ a range of iterative heuristics to minimise the cosine-similarity between texts and high-dimensional feature vectors. A first study shows that users find the automatically generated summaries the most useful when they are generated using word similarity and based on the eight most relevant software artefacts.Mahfouth Alghamdi, Christoph Treude, Markus Wagne

Multi-Document Summarisation from Heterogeneous Software Development Artefacts

Software engineers create a vast number of artefacts during project development; activities, consisting of related information exchanged between developers. Sifting a large amount of information available within a project repository can be time-consuming. In this dissertation, we proposed a method for multi-document summarisation from heterogeneous software development artefacts to help software developers by automatically generating summaries to help them target their information needs. To achieve this aim, we first had our gold-standard summaries created; we then characterised them, and used them to identify the main types of software artefacts that describe developers’ activities in GitHub project repositories. This initial step was important for the present study, as we had no prior knowledge about the types of artefacts linked to developers’ activities that could be used as sources of input for our proposed multi-document summarisation techniques. In addition, we used the gold-standard summaries later to evaluate the quality of our summarisation techniques. We then developed extractive-based multi- document summarisation approaches to automatically summarise software development artefacts within a given time frame by integrating techniques from natural language processing, software repository mining, and data-driven search-based software engineering. The generated summaries were then evaluated in a user study to investigate whether experts considered that the generated summaries mentioned every important project activity that appeared in the gold-standard summaries. The results of the user study showed that generating summaries from different kinds of software artefacts is possible, and the generated summaries are useful in describing a project’s development activities over a given time frame. Finally, we investigated the potential of using source code comments for summarisation by assessing the documented information of Java primitive variables in comments against three types of knowledge. Results showed that the source code comments did contain additional information and could be useful for summarisation of developers’ development activities.Thesis (Ph.D.) -- University of Adelaide, School of Computer Science, 202

An examination of automatic video retrieval technology on access to the contents of an historical video archive

Purpose – This paper aims to provide an initial understanding of the constraints that historical video collections pose to video retrieval technology and the potential that online access offers to both archive and users. Design/methodology/approach – A small and unique collection of videos on customs and folklore was used as a case study. Multiple methods were employed to investigate the effectiveness of technology and the modality of user access. Automatic keyframe extraction was tested on the visual content while the audio stream was used for automatic classification of speech and music clips. The user access (search vs browse) was assessed in a controlled user evaluation. A focus group and a survey provided insight on the actual use of the analogue archive. The results of these multiple studies were then compared and integrated (triangulation). Findings – The amateur material challenged automatic techniques for video and audio indexing, thus suggesting that the technology must be tested against the material before deciding on a digitisation strategy. Two user interaction modalities, browsing vs searching, were tested in a user evaluation. Results show users preferred searching, but browsing becomes essential when the search engine fails in matching query and indexed words. Browsing was also valued for serendipitous discovery; however the organisation of the archive was judged cryptic and therefore of limited use. This indicates that the categorisation of an online archive should be thought of in terms of users who might not understand the current classification. The focus group and the survey showed clearly the advantage of online access even when the quality of the video surrogate is poor. The evidence gathered suggests that the creation of a digital version of a video archive requires a rethinking of the collection in terms of the new medium: a new archive should be specially designed to exploit the potential that the digital medium offers. Similarly, users' needs have to be considered before designing the digital library interface, as needs are likely to be different from those imagined. Originality/value – This paper is the first attempt to understand the advantages offered and limitations held by video retrieval technology for small video archives like those often found in special collections

Aide-mémoire: Improving a Project’s Collective Memory via Pull Request–Issue Links

Links between pull request and the issues they address document and accelerate the development of a software project but are often omitted. We present a new tool, Aide-mémoire, to suggest such links when a developer submits a pull request or closes an issue, smoothly integrating into existing workflows. In contrast to previous state-of-the-art approaches that repair related commit histories, Aide-mémoire is designed for continuous, real-time, and long-term use, employing Mondrian forest to adapt over a project’s lifetime and continuously improve traceability. Aide-mémoire is tailored for two specific instances of the general traceability problem—namely, commit to issue and pull request to issue links, with a focus on the latter—and exploits data inherent to these two problems to outperform tools for general purpose link recovery. Our approach is online, language-agnostic, and scalable. We evaluate over a corpus of 213 projects and six programming languages, achieving a mean average precision of 0.95. Adopting Aide-mémoire is both efficient and effective: A programmer need only evaluate a single suggested link 94% of the time, and 16% of all discovered links were originally missed by developers

Stimulating and Simulating Creativity with Dr Inventor

Dr Inventor is a system that is at once, a computational model of creative thinking and also a tool to ignite the creativity process among its users. Dr Inventor uncovers creative bisociations between semi-structured documents like academic papers, patent applications and psychology materials, by adopting a “big data” perspective to discover creative comparisons. The Dr Inventor system is described focusing on the transformation of this textual information into the graph-structure required by the creative cognitive model. Results are described using data from both psychological test materials and published research papers. The operation of Dr Inventor for both focused creativity and open ended creativity is also outlined

Stimulating and Simulating Creativity with Dr Inventor

Dr Inventor is a system that is at once, a computational model of creative thinking and also a tool to ignite the creativity process among its users. Dr Inventor uncovers creative bisociations between semi-structured documents like academic papers, patent applications and psychology materials, by adopting a “big data” perspective to discover creative comparisons. The Dr Inventor system is described focusing on the transformation of this textual information into the graph-structure required by the creative cognitive model. Results are described using data from both psychological test materials and published research papers. The operation of Dr Inventor for both focused creativity and open ended creativity is also outlined

Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation

This paper surveys the current state of the art in Natural Language Generation (NLG), defined as the task of generating text or speech from non-linguistic input. A survey of NLG is timely in view of the changes that the field has undergone over the past decade or so, especially in relation to new (usually data-driven) methods, as well as new applications of NLG technology. This survey therefore aims to (a) give an up-to-date synthesis of research on the core tasks in NLG and the architectures adopted in which such tasks are organised; (b) highlight a number of relatively recent research topics that have arisen partly as a result of growing synergies between NLG and other areas of artificial intelligence; (c) draw attention to the challenges in NLG evaluation, relating them to similar challenges faced in other areas of Natural Language Processing, with an emphasis on different evaluation methods and the relationships between them.Comment: Published in Journal of AI Research (JAIR), volume 61, pp 75-170. 118 pages, 8 figures, 1 tabl