Large‐scale semi‐automated migration of legacy C/C++ test code

Abstract

This is an industrial experience report on a large semi-automated migration of legacy test code in C and C++. The particular migration was enabled by automating most of the maintenance steps. Without automation this particular large-scale migration would not have been conducted, due to the risks involved in manual maintenance (risk of introducing errors, risk of unexpected rework, and loss of productivity). We describe and evaluate the method of automation we used on this real-world case. The benefits were that by automating analysis, we could make sure that we understand all the relevant details for the envisioned maintenance, without having to manually read and check our theories. Furthermore, by automating transformations we could reiterate and improve over complex and large scale source code updates, until they were "just right."The drawbacks were that, first, we have had to learn new metaprogramming skills. Second, our automation scripts are not readily reusable for other contexts; they were necessarily developed for this ad-hoc maintenance task. Our analysis shows that automated software maintenance as compared to the (hypothetical) manual alternative method seems to be better both in terms of avoiding mistakes and avoiding rework because of such mistakes. It seems that necessary and beneficial source code maintenance need not to be avoided, if software engineers are enabled to create bespoke (and ad-hoc) analysis and transformation tools to support it.

Automatic refactoring of conditions and substitutions for B state transition models

“…For example, automated software development is becoming a trend of SE, ranging from the estimation of development workload and cost, [1][2][3] to program generation and evolution. [4][5][6][7][8] Repair (APR) 9 is a nontrivial task as it concerns not only program diagnosis, which can be achieved by automated testing and verification, but also patch synthesis, which generates repairs from program diagnosis results. APR is beneficial to software practitioners as it reduces the cost of debugging, updating, and maintenance.…”

“…There have been major advances in Software Engineering (SE) techniques with the advent of Artificial Intelligence (AI) in recent years. For example, automated software development is becoming a trend of SE, ranging from the estimation of development workload and cost, 1‐3 to program generation and evolution 4‐8 . In program evolution, Automated Program Repair (APR) 9 is a nontrivial task as it concerns not only program diagnosis, which can be achieved by automated testing and verification, but also patch synthesis, which generates repairs from program diagnosis results.…”