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

On Decomposition of Maximal Satisfiable Subsets

In many areas of computer science, we are given an unsatisfiable formula F in CNF, i.e., a set of clauses, with the goal to analyze the unsatisfiability. A kind of such analysis is to identify Minimal Correction Subsets (MCSes) of F, i.e., minimal subsets of clauses that need to be removed from F to make it satisfiable. Equivalently, one might identify the complements of MCSes, i.e., Maximal Satisfiable Subsets (MSSes) of F. The more MSSes (MCSes) of F are identified, the better insight into the unsatisfiability can be obtained. Hence, there were proposed many algorithms for complete MSS (MCS) enumeration. Unfortunately, the number of MSSes can be exponential w.r.t. |F|, which often makes the complete enumeration practically intractable. In this work, we attempt to cope with the intractability of complete MSS enumeration by initiating the study on MSS decomposition. In particular, we propose several techniques that often allows for decomposing the input formula F into several subformulas. Subsequently, we explicitly enumerate all MSSes of the subformulas, and then combine those MSSes to form MSSes of the original formula F. An extensive empirical study demonstrates that due to the MSS decomposition, the number of MSSes that need to be explicitly identified is often exponentially smaller than the total number of MSSes. Consequently, we are able to improve upon a scalability of contemporary MSS enumeration approaches by many orders of magnitude.22122211