Cmputer and Software Engineering

Managing and controlling interference conditions in multi-threaded programs has been an issue of worry for application developers for a long time. Typically, when write events from two concurrent threads to the same shared variable are not properly protected, an occurrence of the interference bug pattern could be exposed. We propose a mathematical formulation and its resolution to maximize the possibility of exposing occurrences of the interference bug pattern. We formulate and solve the issue as an optimization problem that gives us (1) the optimal position to inject a delay in the execution flow of a thread and (2) the optimal duration for this delay to align at least two different write events in a multi-threaded program. To run the injected threads and calculate the thread execution times for validating the results, we use a virtual platform modelling a perfectly parallel system. All the effects due to the operating system's scheduler or the latencies of hardware components are reduced to zero, exposing only the interactions between threads. To the best of our knowledge, no previous work has formalized the alignment of memory access events to expose occurrences of the interference bug pattern. We use three different algorithms (random, stochastic hill climbing, and simulated annealing) to solve the optimization problem and compare their performance. We carry out experiments on four small synthetic programs and three real-world applications with varying numbers of threads and read/write executions. Our results show that the possibility of exposing interference bug pattern can be significantly enhanced, and that metaheuristics (hill climbing and simulated annealing) provide much better results than a random algorithm.

Developers may introduce anti-patterns in their software systems because of time pressure, lack of understanding, communication, and-or skills. Anti-patterns impede development and maintenance activities by making the source code more difficult to understand. Detecting anti-patterns in a whole software system may be infeasible because of the required parsing time and of the subsequent needed manual validation. Detecting anti-patterns on subsets of a system could reduce costs, effort, and resources. Researchers have proposed approaches to detect occurrences of anti-patterns but these approaches have currently some limitations: they require extensive knowledge of anti-patterns, they have limited precision and recall, and they cannot be applied on subsets of systems. To overcome these limitations, we introduce SVMDetect, a novel approach to detect anti-patterns, based on a machine learning technique-support vector machines. Indeed, through an empirical study involving three subject systems and four anti-patterns, we showed that the accuracy of SVMDetect is greater than of DETEX when detecting anti-patterns occurrences on a set of classes. Concerning, the whole system, SVMDetect is able to find more anti-patterns occurrences than DETEX.

Verification of complex systems with multiple processors is difficult. The reason being that the generation of test cases for the whole system is quite complex. So, the system must be verified in parts and sequentially, i.e., verifying the software, hardware platform separately and the finally software running on the hardware platform. As verification of the MPSoC (Multiple-Systems-on-Chip) platform is beyond the scope of our research, we assume that the MPSoC hardware platform is already verified. Thus, we focus our research on the verification of the MPSoC software (application software running on the MPSoC platform) and the system consisting of the MPSOC software running on the MPSoC platform. Researchers have tried to verify the software portion by generating test cases using metaheutistics, constraint programming and combined metaheuristicconstraint programming approaches. But metaheuristic approaches are not capable of finding good solution as they may get blocked in local optima, whereas constraint programming approaches are not able to generate good test cases when the problem is large and complex. The combined metaheuristic-constraint programming approaches solve these limitations but lose many good test cases when they reduce the domain of the input variables. We want to generate test cases for software while overcoming the limitations mentioned. For this, we propose to combine metaheuristic and constraint programming approaches. In our approach, constraint programming solver will split the input variable domains before reducing them further to be fed into the metaheuristic solver that will generate test cases. Finally, at a later stage of our research, we want to verify the whole system consisting of an application software (DEMOSAIK or FFMPEG 4) running on an MPSoC architecture simulator, the ReSP platform. We propose to generate the test cases from the functional test objectives to check the proper functioning of the software running on the hardware platform. So, we frame the two research questions as: Verification of software by generating test cases so as to satisfy certain coverage criterion and cause the software to fail, and verification of the functional and structural coverage criteria(s) of the system as a whole. We report the results of the preliminary experiments conducted, which helps us to provide a path for the subsequent steps.

Abstract. In this paper, we discuss how a search-based branch coverage approach can be used to design an effective test data generation approach, specifically targeting divide-by-zero exceptions. We first propose a novel testability transformation combining approach level and branch distance. We then use different search strategies, i.e., hill climbing, simulated annealing, and genetic algorithm, to evaluate the performance of the novel testability transformation on a small synthetic example as well as on methods known to throw divide-by-zero exceptions, extracted from real world systems, namely Eclipse and Android. Finally, we also describe how the test data generation for divide-by-zero exceptions can be formulated as a constraint programming problem and compare the resolution of this problem with a genetic algorithm in terms of execution time. We thus report evidence that genetic algorithm using our novel testability transformation out-performs hill climbing and simulated anneali...

Résumé: Like any other complex software system, Service Based Systems (SBSs) must evolve to fit new user requirements and execution contexts. The changes resulting from the evolution of SBSs may degrade their design and quality of service (QoS) and may often cause the appearance of common poor solutions, called Antipatterns. Antipatterns resulting from these changes also hinder the future maintenance and evolution of SBSs.

Abstract—The order in which requirements are implemented in a system affects the value delivered to the final users in the successive releases of the system. Requirements prioritization aims at ranking the requirements so as to trade off user priorities and implementation constraints, such as technical dependencies among requirements and necessarily limited resources allocated to the project. Requirement analysts possess relevant knowledge about the relative importance of requirements.

Abstract The prioritization of requirements is a crucial activity in the early phases of the software development process. It consists of finding an order relation among requirements, considering several requirements characteristics, such as stakeholder preferences, technical constraints, implementation costs and user perceived value.

Abstract Software maintenance can become monotonous and expensive due to ignorance and misapplication of appropriate design patterns during the early phases of design and development. To have a good and reusable system, designers and developers must be aware of large information set and many quality concerns, eg, design patterns. Systems with correct design pattern may ensure easy maintenance and evolution.

This paper presents an unsupervised and domain independent semantic analysis that outputs two types of formal representations: discourse representations structures and flat scope-free logical forms. This semantic analysis is built on top of dependency relations produced by a statistical syntactic parser, and is generated by a grammar of patterns named α-grammar. The interest of this grammar lies in building a clear linguistically-grounded syntax-semantic interface using a representation (dependencies) commonly used in the natural language processing community. The paper also explains how semantic representations can be annotated using an upper-level ontology, thus enabling further inference capabilities. The evaluation of the α-Grammar on a hand-made gold standard and on texts from the STEP 2008 Shared Task competition shows the interest of the approach.

We describe our contribution to the Generation Challenge 2010 for the tasks of Named Entity Recognition and coreference detection (GREC-NER). To extract the NE and the referring expressions, we employ a combination of a Part of Speech Tagger and the Conditional Random Fields (CRF) learning technique. We finally experiment an original algorithm to detect co-references. We conclude with discussion about our system performances.

This paper describes a method to perform automated semantic annotation of named entities contained in large corpora. The semantic annotation is made in the context of the Semantic Web. The method is based on an algorithm that compares the set of words that appear before and after the name entity with the content of Wikipedia articles, and identifies the more relevant one by means of a similarity measure. It then uses the link that exists between the selected Wikipedia entry and the corresponding RDF description in the Linked Data project to establish a connection between the named entity and some URI in the Semantic Web. We present our system, discuss its architecture, and describe an algorithm dedicated to ontological disambiguation of named entities contained in large-scale corpora. We evaluate the algorithm, and present our results.

The future of high-performance computing is aligning itself towards the efficient use of highly parallel computing environments. One application where the use of massive parallelism comes instinctively is Monte Carlo simulations, where a large number of independent events have to be simulated. At the core of the Monte Carlo simulation lies the Random Number Generator (RNG).