Machine Learning for Software Engineering: A Tertiary Study

Abstract

Machine learning (ML) techniques increase the effectiveness of software engineering (SE) lifecycle activities. We systematically collected, quality-assessed, summarized, and categorized 83 reviews in ML for SE published between 2009–2022, covering 6 117 primary studies. The SE areas most tackled with ML are software quality and testing, while human-centered areas appear more challenging for ML. We propose a number of ML for SE research challenges and actions including: conducting further empirical validation and industrial studies on ML; reconsidering deficient SE methods; documenting and automating data collection and pipeline processes; reexamining how industrial practitioners distribute their proprietary data; and implementing incremental ML approaches.

Drone Technologies: A Tertiary Systematic Literature Review on a Decade of Improvements

“…The tertiary study on Machine Learning for Software Engineering [12], shows an average quality score around 3, rated on the same criteria as this TLR. Whilst the other UAVrelated TLR scores a 0.3 [13], although missing one of the four DARE criteria in their assessment.…”

“…A TLR is performed to consolidate information across fields; identify research topics; aggregate scattered data; find limitations and assess the quality of SLRs with the DARE rating scheme [11]. In [12] for example, the TLR method from [9] was adapted to Machine Learning in Software Engineering. The impact of Machine Learning on the field was assessed and a classification scheme for categorizing applications was presented.…”

A Review of Port Decarbonisation Options: Identified Opportunities for Deploying Hydrogen Technologies

“…The approach is a 'review of review papers', which is termed a tertiary review paper [26,69]. Tertiary reviews are defined in [70], with examples in [71,72]. They are not commonly used across engineering disciplines, but they are effective at summarising large literature bodies.…”

Roadmap Analysis of Artificial Intelligence Engineering Method

“…These encompass data analysis, software testing, project management, system optimization, and user interface interaction. For instance, machine learning serves to construct predictive models capable of deriving insights from data, thereby facilitating decision-making [4]. Deep learning, conversely, is employed to process intricate data, such as images or sound, and acquire profound patterns [5].…”