An application of the event calculus for representing the history of a software project

This paper presents the theory and implementation of a logic based discrete event simulation system ECSIM, Event Calculus SIMulation. ECSIM’s representation language is PROLOG extended with temporal predicates derived from the event calculus. The theory defines the truth value of a property given a history of events. ECSIM can represent actions that happen at a particular point in time, and activities that happen over a period of time; it can represent properties that change discretely and continuously over time. ECSIM’s scheduling algorithm uses activity scanning to generate event notices for all future activities. ECSIM’s major distinction with other simulation systems is its reference to the complete history of simulated time. A given event schedule can be analyzed by deriving the properties of the world at any time in the simulated history. ECSIM’s logic programming framework enables classical simulation to be extended with explanation generation, inductive learning, planning, decision support, simulation of intelligent agents, and symbolic simulation.

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Analysing Time-Stamped Co-Editing Networks in Software Development Teams using git2net

Empirical Software Engineering ◽

10.1007/s10664-020-09928-2 ◽

2021 ◽

Vol 26 (4) ◽

Author(s):

Christoph Gote ◽

Ingo Scholtes ◽

Frank Schweitzer

Keyword(s):

Software Engineering ◽

Computational Social Science ◽

Software Project ◽

High Resolution Data ◽

Software Repositories ◽

Fine Grained ◽

Development Teams ◽

History Of ◽

Software Development Teams ◽

Advance Theory

AbstractData from software repositories have become an important foundation for the empirical study of software engineering processes. A recurring theme in the repository mining literature is the inference of developer networks capturing e.g. collaboration, coordination, or communication from the commit history of projects. Many works in this area studied networks of co-authorship of software artefacts, neglecting detailed information on code changes and code ownership available in software repositories. To address this issue, we introduce , a scalable software that facilitates the extraction of fine-grained co-editing networks in large repositories. It uses text mining techniques to analyse the detailed history of textual modifications within files. We apply our tool in two case studies using repositories of multiple Open Source as well as a proprietary software project. Specifically, we use data on more than 1.2 million commits and more than 25,000 developers to test a hypothesis on the relation between developer productivity and co-editing patterns in software teams. We argue that opens up an important new source of high-resolution data on human collaboration patterns that can be used to advance theory in empirical software engineering, computational social science, and organisational studies.

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A Change Recommendation Approach Using Change Patterns of a Corresponding Test File

Symmetry ◽

10.3390/sym10110534 ◽

2018 ◽

Vol 10 (11) ◽

pp. 534

Author(s):

Jungil Kim ◽

Eunjoo Lee

Keyword(s):

Open Source ◽

Software Project ◽

Meaningful Change ◽

Software Projects ◽

Source File ◽

Naming Convention ◽

History Of ◽

Test File ◽

Source Test

Change recommendation improves the development speed and quality of software projects. Through change recommendation, software project developers can find the relevant source files that they must change for their modification tasks. In an existing change-recommendation approach based on the change history of source files, the reliability of the recommended change patterns for a source file is determined according to the change history of the source file. If a source file has insufficient change history to identify its change patterns or has frequently been changed with unrelated source files, the existing change-recommendation approach cannot identify meaningful change patterns for the source file. In this paper, we propose a novel change-recommendation approach to resolve the limitation of the existing change-recommendation method. The basic idea of the proposed approach is to consider the change history of a test file corresponding to a given source file. First, the proposed approach identifies the test file corresponding to a given source file by using a source–test traceability linking method based on the popular naming convention rule. Then, the change patterns of the source and test files are identified according to their change histories. Finally, a set of change recommendations is constructed using the identified change patterns. In an experiment involving six open-source projects, the accuracy of the proposed approach is evaluated. The results show that the accuracy of the proposed approach can be significantly improved from 21% to 62% compared with the existing approach.

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