Recovering software architecture from the names of source files

Abstract

We discuss how to extract a useful set of subsystems from a set of source-code file names. This problem is challenging because, in many legacy systems, there are thousands of files with names that are very short and cryptic. At the same time the problem is important because software engineers often find it difficult to understand such systems. We propose a general algorithm to cluster files based on their names, and a set of alternative methods for implementing the algorithm. One of the key tasks is picking candidate words that we will try to identify in file names. We do this by a) iteratively decomposing file names, b) finding common substrings, and c) choosing words in routine names, in an English dictionary or in source code comments. In addition, we investigate generating abbreviations from the candidate words in order to find matches in file names, as well as how to split file names into components given no word markers. To compare and evaluate our approaches, we present two experiments. The first compares the "concepts" found in each file name by each method to the results of manually decomposing file names. The second experiment compares automatically generated subsystems to subsystem examples proposed by experts. We conclude that two methods are most effective: Extracting concepts using common substrings, and extracting those concepts that relate to the names of routines in the files.

Supervised software modularisation

“…Table I summarises the case studies used in the experiment. The SLoC metric is taken as the current project size as reported by Ohloh, an open source project metrics tracker 2 . The "Component Used" column identifies which part of the system was used in cases where the product comprises multiple JAR files.…”

“…Both approaches ultimately use certain indicators in the system to assign elements into appropriate groups. Previous approaches have selected indicators to be file names [2], the connectivity of the files [3] or other features [4], [5], [6], [7], [8]. They are unsupervised in the sense that there is no (or at best very limited) means to externally guide the process to improve the final outcome.…”

Splitting a large software repository for easing future software evolution—an industrial experience report

“…Anquetil and Lethbridge on the other hand use file names for creating an abstraction of the architecture using clustering [25]. They also provide an overview of a range of issues to tackle when adopting cluster analysis, and illustrate the effects on several open source systems as well as a system comprising two million lines of code [26].…”

Reducing operational costs through MIPS management

“…In [46] the authors show how source code analysis supports reduction of costs in IT transformation projects. Literature provides examples of its application in supporting recovery of software architecture [3] or migration of the IT-portfolio to a service oriented architecture (SOA) model [24]. There are also numerous instances of automated software modifications [47,59,58] aided with code analysis.…”

“…In this query we found programming constructs labeled by us earlier as AGGF, ORDER, WHRE 3 The country code check, which was carried out as first, was not restricting the dataset well enough and thus hampering the performance. All five cursors had their WHERE clauses rewritten by changing the order in which the predicates are checked.…”