Mining Treatment-Outcome Constructs from Sequential Software Engineering Data

Maleknaz Nayebi

doi:10.1109/TSE.2019.2892956

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Mining Treatment-Outcome Constructs from Sequential Software Engineering Data

2021-02-01

Maleknaz Nayebi *

Guenther Ruhe

Thomas Zimmermann

DOI：10.1109/TSE.2019.2892956

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Abstract

En 中文

Many investigations in empirical software engineering look at sequences of data resulting from development or management processes. In this paper, we propose an analytical approach called the Gandhi-Washington Method (GWM) to investigate the impact of recurring events in software projects. GWM takes an encoding of events and activities provided by a software analyst as input. It uses regular expressions to automatically condense and summarize information and infer treatments. Relating the treatments to the outcome through statistical tests, treatment-outcome constructs are automatically mined from the data. The output of GWM is a set of treatment-outcome constructs. Each treatment in the set of mined constructs is significantly different from the other treatments considering the impact on the outcome and/or is structurally different from other treatments considering the sequence of events. We describe GWM and classes of problems to which GWM can be applied. We demonstrate the applicability of this method for empirical studies on sequences of file editing, code ownership, and release cycle time.

Keywords:

Software

Computer bugs

Encoding

Data mining

Software engineering

Testing

Itemsets

Pattern mining

mining software repositories

regular expressions

analytics

release cycle time patterns

code ownership

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