An Empirical Study of Cohesion and Coupling: Balancing Optimisation and Disruption

Search based software engineering has been extensively applied to the problem of finding improved modular structures that maximise cohesion and minimise coupling. However, there has, hitherto, been no longitudinal study of developers’ implementations, over a series of sequential releases. Moreover, results validating whether developers respect the fitness functions are scarce, and the potentially disruptive effect of search-based remodularisation is usually overlooked. We present an empirical study of 233 sequential releases of 10 different systems; the largest empirical study reported in the literature so far, and the first longitudinal study. Our results provide evidence that developers do, indeed, respect the fitness functions used to optimise cohesion/coupling (they are statistically significantly better than arbitrary choices with p << 0.01), yet they also leave considerable room for further improvement (cohesion/coupling can be improved by 25% on average). However, we also report that optimising the structure is highly disruptive (on average more than 57% of the structure must change), while our results reveal that developers tend to avoid such disruption. Therefore, we introduce and evaluate a multi-objective evolutionary approach that minimises disruption while maximising cohesion/coupling improvement. This allows developers to balance reticence to disrupt existing modular structure, against their competing need to improve cohesion and coupling. The multi-objective approach is able to find modular structures that improve the cohesion of developers’ implementations by 22.52%, while causing an acceptably low level of disruption (within that already tolerated by developers)

Leveraging Automated Unit Tests for Unsupervised Code Translation

With little to no parallel data available for programming languages, unsupervised methods are well-suited to source code translation. However, the majority of unsupervised machine translation approaches rely on back-translation, a method developed in the context of natural language translation and one that inherently involves training on noisy inputs. Unfortunately, source code is highly sensitive to small changes; a single token can result in compilation failures or erroneous programs, unlike natural languages where small inaccuracies may not change the meaning of a sentence. To address this issue, we propose to leverage an automated unit-testing system to filter out invalid translations, thereby creating a fully tested parallel corpus. We found that fine-tuning an unsupervised model with this filtered data set significantly reduces the noise in the translations so-generated, comfortably outperforming the state-of-the-art for all language pairs studied. In particular, for Java→Python and Python→C++ we outperform the best previous methods by more than 16% and 24% respectively, reducing the error rate by more than 35%

Backward conditioning: A new program specialisation technique and its application to program comprehension

This paper introduces backward conditioning. Like forward conditioning (used in conditioned slicing), backward conditioning consists of specialising a program with respect to a condition inserted into the program. However, whereas forward conditioning deletes statements which are not executed when the initial state satisfies the condition, backward conditioning deletes statements which cannot cause execution to enter a state which satisfies the condition. The relationship between backward and forward conditioning is reminiscent of the relationship between backward and forward slicing. Forward conditioning addresses program comprehension questions of the form `what happens if the program starts in a state satisfying condition c?`, whereas backward conditioning addresses questions of the form `what parts of the program could potentially lead to the program arriving in a state satisfying condition c?' The paper illustrates the use of backward conditioning as a program comprehension assistant and presents an algorithm for constructing backward conditioned programs

An empirical investigation into branch coverage for C programs using CUTE and AUSTIN

Automated test data generation has remained a topic of considerable interest for several decades because it lies at the heart of attempts to automate the process of Software Testing. This paper reports the results of an empirical study using the dynamic symbolic-execution tool. CUTE, and a search based tool, AUSTIN on five non-trivial open source applications. The aim is to provide practitioners with an assessment of what can be achieved by existing techniques with little or no specialist knowledge and to provide researchers with baseline data against which to measure subsequent work. To achieve this, each tool is applied 'as is', with neither additional tuning nor supporting harnesses and with no adjustments applied to the subject programs under test. The mere fact that these tools can be applied 'out of the box' in this manner reflects the growing maturity of Automated test data generation. However, as might be expected, the study reveals opportunities for improvement and suggests ways to hybridize these two approaches that have hitherto been developed entirely independently. (C) 2010 Elsevier Inc. All rights reserved

05451 Abstracts Collection -- Beyond Program Slicing

From 06.11.05 to 11.11.05, the Dagstuhl Seminar 05451 ``Beyond Program Slicing\u27\u27 was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available