Reducing the Effort for Systematic Reviews in Software Engineering

Tracking #: 553-1533

Responsible editor: 

James McCusker

Submission Type: 

Research Paper


Context. Systematic Reviews (SRs) are means for collecting and synthesizing evidence from the identification and analysis of relevant studies from multiple sources. To this aim, they use a well-defined methodology meant to mitigate the risks of biases and ensure repeatability for later updates. SRs, however, involve significant effort. Goal. The goal of this paper is to introduce a novel methodology that reduces the amount of manual tedious tasks involved in SRs while taking advantage of the value provided by human expertise. Method. Starting from current methodologies for SRs, we replaced the steps of keywording and data extraction with an automatic methodology for generating a domain ontology and classifying the primary studies. This methodology has been applied in the software engineering sub-area of software architecture and evaluated by human annotators. Results. The result is a novel Expert-Driven Automatic Methodology, EDAM, for assisting researchers in performing SRs. EDAM combines ontology-learning techniques and semantic technologies with the human-in-the-loop. The first (thanks to automation) fosters scalability, objectivity, reproducibility and granularity of the studies; the second allows tailoring to the specific focus of the study at hand and knowledge reuse from domain experts. We evaluated EDAM on the field of Software Architecture against six senior researchers. As a result, we found that the performance of the senior researchers in classifying papers was not statistically significantly different from EDAM. Conclusions. Thanks to automation of the less-creative steps in SRs, our methodology allows researchers to skip the tedious tasks of keywording and manually classifying primary studies, thus freeing effort for the analysis and the discussion.



  • Reviewed

Data repository URLs: 

Date of Submission: 

Monday, January 7, 2019

Date of Decision: 

Monday, March 18, 2019



Solicited Reviews:

1 Comment

Meta-Review by Editor

Thank you for submitting this paper. The ability to There are some significant issues with both the presentation and content of this paper. Reviewers have conflicting opinions about the novelty of this work. This must be clarified in the next submission. Additionally, the issues with the evaluation must be fixed. Most importantly, the claims of the paper must be very strongly supported by the evaluation. Currently, the title and abstract suggest much broader claims than the evaluation provides, suggesting that more of the SR process is solved by your methods.

James McCusker (