Finding Regressions in Projects under Version Control Systems

Version Control Systems (VCS) are frequently used to support development of large-scale software projects. A typical VCS repository of a large project can contain various intertwined branches consisting of a large number of commits. If some kind of unwanted behaviour (e.g. a bug in the code) is found in the project, it is desirable to find the commit that introduced it. Such commit is called a regression point. There are two main issues regarding the regression points. First, detecting whether the project after a certain commit is correct can be very expensive as it may include large-scale testing and/or some other forms of verification. It is thus desirable to minimise the number of such queries. Second, there can be several regression points preceding the actual commit; perhaps a bug was introduced in a certain commit, inadvertently fixed several commits later, and then reintroduced in a yet later commit. In order to fix the actual commit it is usually desirable to find the latest regression point. The currently used distributed VCS contain methods for regression identification, see e.g. the git bisect tool. In this paper, we present a new regression identification algorithm that outperforms the current tools by decreasing the number of validity queries. At the same time, our algorithm tends to find the latest regression points which is a feature that is missing in the state-of-the-art algorithms. The paper provides an experimental evaluation of the proposed algorithm and compares it to the state-of-the-art tool git bisect on a real data set

Quantitative Assessment of the Impact of Automatic Static Analysis Issues on Time Efficiency

Background: Automatic Static Analysis (ASA) tools analyze source code and look for code patterns (aka smells) that might cause defective behavior or might degrade other dimensions of software quality, e.g. efficiency. There are many potentially negative code patterns, and ASA tools typically report a huge list of them even in small programs. Moreover, so far, little evidence is available about the negative impact on performance of code patterns identified by such tools. A consequence is that programmers cannot appreciate the benefits of ASA tools and tend not to include them in their workflow. Aims: Quantitatively assess the impact of issues signaled by ASA tools on time efficiency. Method: We select 20 issues and for each of them we set up two source code fragments: one containing the issue and the corresponding refactored version, functionally identical but without the issue. We set up three different platforms, isolated from network and other user programs, then we execute the code fragments, and measure the execution time of both code versions. Results: We find that eleven issues have an actual negative impact on performance. We also compute for each issue an estimation for the delay provoked by a single execution. Conclusions: We produce a set of issues with a verified negative impact on performance. They can be checked easily with an analysis tool and code can be refactored to obtain a provably more efficient code. We also provide the estimated delay cost of each issue in the environments where we conduct the tests. These results can be improved with the help of other researchers: repeating the tests in several platforms would make it possible to build up a wider benchmar

Overcoming Language Dichotomies: Toward Effective Program Comprehension for Mobile App Development

Mobile devices and platforms have become an established target for modern software developers due to performant hardware and a large and growing user base numbering in the billions. Despite their popularity, the software development process for mobile apps comes with a set of unique, domain-specific challenges rooted in program comprehension. Many of these challenges stem from developer difficulties in reasoning about different representations of a program, a phenomenon we define as a "language dichotomy". In this paper, we reflect upon the various language dichotomies that contribute to open problems in program comprehension and development for mobile apps. Furthermore, to help guide the research community towards effective solutions for these problems, we provide a roadmap of directions for future work.Comment: Invited Keynote Paper for the 26th IEEE/ACM International Conference on Program Comprehension (ICPC'18