Less, but Stronger: On the Value of Strong Heuristics in Semi-supervised Learning for Software Analytics

• URL: http://arxiv.org/abs/2302.01997v1
• Date: Fri, 3 Feb 2023 20:59:09 GMT
• Title: Less, but Stronger: On the Value of Strong Heuristics in Semi-supervised Learning for Software Analytics
• Authors: Huy Tu and Tim Menzies
• Abstract summary: Semi-supervised learners (SSL) can extrapolate labels from a small number of examples to the rest of the data. This paper argues that such strong'' algorithms perform better than those standard, weaker, SSL algorithms.
• Score: 31.13621632964345
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In many domains, there are many examples and far fewer labels for those examples; e.g. we may have access to millions of lines of source code, but access to only a handful of warnings about that code. In those domains, semi-supervised learners (SSL) can extrapolate labels from a small number of examples to the rest of the data. Standard SSL algorithms use ``weak'' knowledge (i.e. those not based on specific SE knowledge) such as (e.g.) co-train two learners and use good labels from one to train the other. Another approach of SSL in software analytics is potentially use ``strong'' knowledge that use SE knowledge. For example, an often-used heuristic in SE is that unusually large artifacts contain undesired properties (e.g. more bugs). This paper argues that such ``strong'' algorithms perform better than those standard, weaker, SSL algorithms. We show this by learning models from labels generated using weak SSL or our ``stronger'' FRUGAL algorithm. In four domains (distinguishing security-related bug reports; mitigating bias in decision-making; predicting issue close time; and (reducing false alarms in static code warnings), FRUGAL required only 2.5% of the data to be labeled yet out-performed standard semi-supervised learners that relied on (e.g.) some domain-independent graph theory concepts. Hence, for future work, we strongly recommend the use of strong heuristics for semi-supervised learning for SE applications. To better support other researchers, our scripts and data are on-line at https://github.com/HuyTu7/FRUGAL.

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