There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

(2002). Getting to Yes: Negotiating Agreement without Giving in. Quality Management Journal: Vol. 9, No. 2, pp. 73-74.

Getting to Yes: Negotiating Agreement without Giving in

Software forges like GitHub host millions of repositories. Software engineering researchers have been able to take advantage of such a large corpora of potential study subjects with the help of tools like GHTorrent and Boa. However, the simplicity in querying comes with a caveat: there are limited means of separating the signal (e.g. repositories containing engineered software projects) from the noise (e.g. repositories containing home work assignments). The proportion of noise in a random sample of repositories could skew the study and may lead to researchers reaching unrealistic, potentially inaccurate, conclusions. We argue that it is imperative to have the ability to sieve out the noise in such large repository forges. We propose a framework, and present a reference implementation of the framework as a tool called reaper, to enable researchers to select GitHub repositories that contain evidence of an engineered software project. We identify software engineering practices (called dimensions) and propose means for validating their existence in a GitHub repository. We used reaper to measure the dimensions of 1,857,423 GitHub repositories. We then used manually classified data sets of repositories to train classifiers capable of predicting if a given GitHub repository contains an engineered software project. The performance of the classifiers was evaluated using a set of 200 repositories with known ground truth classification. We also compared the performance of the classifiers to other approaches to classification (e.g. number of GitHub Stargazers) and found our classifiers to outperform existing approaches. We found stargazers-based classifier (with 10 as the threshold for number of stargazers) to exhibit high precision (97%) but an inversely proportional recall (32%). On the other hand, our best classifier exhibited a high precision (82%) and a high recall (86%). The stargazer-based criteria offers precision but fails to recall a significant portion of the population.

Curating GitHub for engineered software projects

This paper summarizes the current state of the art and recent trends in software engineering economics. It provides an overview of economic analysis techniques and their applicability to software engineering and management. It surveys the field of software cost estimation, including the major estimation techniques available, the state of the art in algorithmic cost models, and the outstanding research issues in software cost estimation.

Software engineering economics

Software traceability is a sought-after, yet often elusive quality in software-intensive systems. Required in safety-critical systems by many certifying bodies, such as the USA Federal Aviation Authority, software traceability is an essential element of the software development process. In practice, traceability is often conducted in an ad-hoc, after-the-fact manner and, therefore, its benefits are not always fully realized. Over the past decade, researchers have focused on specific areas of the traceability problem, developing more sophisticated tooling, promoting strategic planning, applying information retrieval techniques capable of semi-automating the trace creation and maintenance process, developing new trace query languages and visualization techniques that use trace links, and applying traceability in specific domains such as Model Driven Development, product line systems, and agile project environments. In this paper, we build upon a prior body of work to highlight the state-of-the-art in software traceability, and to present compelling areas of research that need to be addressed.

/pdf/software-traceability-trends-and-future-directions-39ndkmlc9z.pdf

Software traceability: trends and future directions

Emotions have a high impact in productivity, task quality, creativity, group rapport and job satisfaction. In this work we use lexical sentiment analysis to study emotions expressed in commit comments of different open source projects and analyze their relationship with different factors such as used programming language, time and day of the week in which the commit was made, team distribution and project approval. Our results show that projects developed in Java tend to have more negative commit comments, and that projects that have more distributed teams tend to have a higher positive polarity in their emotional content. Additionally, we found that commit comments written on Mondays tend to a more negative emotion. While our results need to be confirmed by a more representative sample they are an initial step into the study of emotions and related factors in open source projects.

Sentiment analysis of commit comments in GitHub: an empirical study

In recent years, models are increasingly used throughout the entire lifecycle in software development projects. In effect, the need for collaborating on these models emerged, requiring change tracking and versioning. However, many researchers have shown that existing methods and tools for Version Control (VC) do not work well on graph-like models, such as UML, SysML or domain-specific modeling languages. To alleviate this, alternative techniques and methods have been proposed which can be classified into state-based and operation-based approaches. Existing research shows advantages of operation-based over state-based approaches in selected use cases, such as conflict detection or merging. However, there are only few results available on the advantages of operation-based approaches in the most common use case of a VC system: review and understand change. In this paper, we present and discuss both approaches and their use cases. Moreover, we present the results of an empirical study to compare a state-based with an operation-based approach for the use case of reviewing and understanding change. For this study, we have mined an operation-based model repository and interviewed users to assess their understanding of randomly selected changes. Our results indicate that users better understand complex changes in the operation-based representation.

http://spes2020.informatik.tu-muenchen.de/results/D-1-4-A-5_ComparingStateAndOperationBasedChangeTrackingOnModels.pdf

Comparing State- and Operation-Based Change Tracking on Models

Traceability supports users in describing and tracking the relationships between software artifacts. Techniques such as traceability matrices and graphs visualize these relationships and help users to access and understand them. Researchers agree that different visualization techniques add valuable information in different contexts. However, there is an ambiguity which visualization is suitable for which context. To clarify this we conducted a comparative study of common visualization techniques, including an experiment and interviews with 24 participants.

We found that traceability matrices and graphs are most preferred in management tasks, while hyperlinks are preferred in implementation and testing tasks. Traceability lists seem to be the least attractive technique for most participants. Graphs are preferred to navigate linked artifacts, while matrices are appropriate for overview. Hyperlinks are regarded to fit for fine-grained information. Participants stressed the importance of visualizing semantics of artifacts and links. Our finding also indicates that users are not always able to choose the most suitable visualization.

Which traceability visualization is suitable in this context? a comparative study

Requirements engineering is crucial for software projects, but formal requirements engineering is often ignored in scientific software projects. Scientists do not often see the benefit of di- recting their time and effort towards documenting requirements. Additionally, there is a lack of requirements engineering knowledge amongst scientists who develop software. We aim at helping scientists to easily recover and reuse requirements without acquiring prior requirements engineering knowledge. We apply an automated approach to extract requirements for scientific software from available knowledge sources, such as user manuals and project reports. The ap- proach employs natural language processing techniques to match defined patterns in input text. We have evaluated the approach in three different scientific domains, namely seismology, build- ing performance and computational fluid dynamics. The evaluation results show that 78–97% of the extracted requirement candidates are correctly extracted as early requirements.

Automated Requirements Extraction for Scientific Software

Requirements engineering is a core activity in software engineering. However, formal requirements engineering methodologies and documented requirements are often missing in scientific computing projects. We claim that there is a need for methodologies, which capture requirements for scientific computing projects, because traditional requirements engineering methodologies are difficult to apply in this domain. We propose a novel domain specific requirements model to meet this need. We conducted an exploratory experiment to evaluate the usage of this model in scientific computing projects. The results indicate that the proposed model facilitates the communication across the domain boundary, which is between the scientific computing domain and the software engineering domain. It supports requirements elicitation for the projects efficiently.

Yang Li

Papers

Sentiment analysis of commit comments in GitHub: an empirical study

Comparing State- and Operation-Based Change Tracking on Models

Which traceability visualization is suitable in this context? a comparative study

Automated Requirements Extraction for Scientific Software

A domain specific requirements model for scientific computing (NIER track)