  Background: Sub-optimal code is prevalent in software systems. Developers may
write low-quality code due to many reasons, such as lack of technical
knowledge, lack of experience, time pressure, management decisions, and even
unhappiness. Once sub-optimal code is unknowingly (or knowingly) integrated
into the codebase of software systems, its accumulation may lead to large
maintenance costs and technical debt. Stack Overflow is a popular website for
programmers to ask questions and share their code snippets. The crowdsourced
and collaborative nature of Stack Overflow has created a large source of
programming knowledge that can be leveraged to assist developers in their
day-to-day activities. Objective: In this paper, we present an exploratory
study to evaluate the usefulness of recommending code improvements based on
Stack Overflow answers' edits. Method: We propose Matcha, a code recommendation
tool that leverages Stack Overflow code snippets with version history and code
clone search techniques to identify sub-optimal code in software projects and
suggest their optimised version. By using SOTorrent and GitHub datasets, we
will quali-quantitatively investigate the usefulness of recommendations given
by \textsc{Matcha} to developers using manual categorisation of the
recommendations and acceptance of pull-requests to open-source projects.
