Spectrum-Based Fault Localization in Model Transformations

Model transformations play a cornerstone role in Model-Driven Engineering (MDE), as they provide the essential mechanisms for manipulating and transforming models. The correctness of software built using MDE techniques greatly relies on the correctness of model transformations. However, it is challenging and error prone to debug them, and the situation gets more critical as the size and complexity of model transformations grow, where manual debugging is no longer possible. Spectrum-Based Fault Localization (SBFL) uses the results of test cases and their corresponding code coverage information to estimate the likelihood of each program component (e.g., statements) of being faulty. In this article we present an approach to apply SBFL for locating the faulty rules in model transformations. We evaluate the feasibility and accuracy of the approach by comparing the effectiveness of 18 different stateof- the-art SBFL techniques at locating faults in model transformations. Evaluation results revealed that the best techniques, namely Kulcynski2, Mountford, Ochiai, and Zoltar, lead the debugger to inspect a maximum of three rules to locate the bug in around 74% of the cases. Furthermore, we compare our approach with a static approach for fault localization in model transformations, observing a clear superiority of the proposed SBFL-based method.Comisión Interministerial de Ciencia y Tecnología TIN2015-70560-RJunta de Andalucía P12-TIC-186

How functional programming mattered

In 1989 when functional programming was still considered a niche topic, Hughes wrote a visionary paper arguing convincingly ‘why functional programming matters’. More than two decades have passed. Has functional programming really mattered? Our answer is a resounding ‘Yes!’. Functional programming is now at the forefront of a new generation of programming technologies, and enjoying increasing popularity and influence. In this paper, we review the impact of functional programming, focusing on how it has changed the way we may construct programs, the way we may verify programs, and fundamentally the way we may think about programs

Improving students' code correctness and test completeness by informal specifications

The quality of software produced by students is often poor. How to teach students to develop good quality software has long been a topic in computer science education and research. We must conclude that we still do not have a good answer to this question. Specifications are necessary to determine the correctness of software, to develop error-free software and to write complete tests. Several attempts have been made to teach students to write specifications before writing code. So far, that has not proven to be very successful: Students do not like to write a specification and do not see the benefits of writing specifications. In this paper we focus on the use of informal specifications. Instead of teaching students how to write specifications, we teach them how to use informal specifications to develop correct software. The results were surprising: the number of errors in software and the completeness of tests both improved considerably and, most importantly, students really appreciate the specifications. We think that if students appreciate specification, we have a key to teach them how to specify and to appreciate its value.Comment: 14 page

Truthful Online Scheduling with Commitments

We study online mechanisms for preemptive scheduling with deadlines, with the goal of maximizing the total value of completed jobs. This problem is fundamental to deadline-aware cloud scheduling, but there are strong lower bounds even for the algorithmic problem without incentive constraints. However, these lower bounds can be circumvented under the natural assumption of deadline slackness, i.e., that there is a guaranteed lower bound $s > 1$ on the ratio between a job's size and the time window in which it can be executed. In this paper, we construct a truthful scheduling mechanism with a constant competitive ratio, given slackness $s > 1$. Furthermore, we show that if $s$ is large enough then we can construct a mechanism that also satisfies a commitment property: it can be determined whether or not a job will finish, and the requisite payment if so, well in advance of each job's deadline. This is notable because, in practice, users with strict deadlines may find it unacceptable to discover only very close to their deadline that their job has been rejected

Quantitative multi-objective verification for probabilistic systems

We present a verification framework for analysing multiple quantitative objectives of systems that exhibit both nondeterministic and stochastic behaviour. These systems are modelled as probabilistic automata, enriched with cost or reward structures that capture, for example, energy usage or performance metrics. Quantitative properties of these models are expressed in a specification language that incorporates probabilistic safety and liveness properties, expected total cost or reward, and supports multiple objectives of these types. We propose and implement an efficient verification framework for such properties and then present two distinct applications of it: firstly, controller synthesis subject to multiple quantitative objectives; and, secondly, quantitative compositional verification. The practical applicability of both approaches is illustrated with experimental results from several large case studies

Arís 2.1: Adapting Arís for Object Oriented Language

In the software development area, software verification is important such that it can guarantee the software fulfills its requirements. Despite its importance, verifying software is difficult to achieve. Additional knowledge and effort are needed to write specification especially if the software is complex and big in size. Nevertheless, there are some software that already have verified specifications. This project will focus on extending Arís (Analogical Reasoning for reuse of Implementation & Specification) which has been developed to increase verified software by reusing and transferring the specification from a similar implementation to a target code. The extension is done to facilitate specification transferring to program written in language other than C#, in this case Java. This extension will add functions to existing Arís that will receive Conceptual Graphs representation of a program and write the specification to a file. Another companion system is also built from Java to generate the Conceptual Graphs in Conceptual Graph Interchange Format (CGIF) and transform the Spec# specification to JML. Finally, this new system is evaluated by running some testing. From the result that we have, we can conclude that the building of conceptual graph and the specification transformation is the most difficult part in our system

Arís 2.1: Adapting Arís for Object Oriented Language

In the software development area, software verification is important such that it can guarantee the software fulfills its requirements. Despite its importance, verifying software is difficult to achieve. Additional knowledge and effort are needed to write specification especially if the software is complex and big in size. Nevertheless, there are some software that already have verified specifications. This project will focus on extending Arís (Analogical Reasoning for reuse of Implementation & Specification) which has been developed to increase verified software by reusing and transferring the specification from a similar implementation to a target code. The extension is done to facilitate specification transferring to program written in language other than C#, in this case Java. This extension will add functions to existing Arís that will receive Conceptual Graphs representation of a program and write the specification to a file. Another companion system is also built from Java to generate the Conceptual Graphs in Conceptual Graph Interchange Format (CGIF) and transform the Spec# specification to JML. Finally, this new system is evaluated by running some testing. From the result that we have, we can conclude that the building of conceptual graph and the specification transformation is the most difficult part in our system