On guiding the augmentation of an automated test suite via mutation analysis

Mutation testing has traditionally been used as a defect injection technique to assess the effectiveness of a test suite as represented by a “mutation score.” Recently, mutation testing tools have become more efficient, and industrial usage of mutation analysis is experiencing growth. Mutation analysis entails adding or modifying test cases until the test suite is sufficient to detect as many mutants as possible and the mutation score is satisfactory. The augmented test suite resulting from mutation analysis may reveal latent faults and provides a stronger test suite to detect future errors which might be injected. Software engineers often look for guidance on how to augment their test suite using information provided by line and/or branch coverage tools. As the use of mutation analysis grows, software engineers will want to know how the emerging technique compares with and/or complements coverage analysis for guiding the augmentation of an automated test suite. Additionally, software engineers can benefit from an enhanced understanding of efficient mutation analysis techniques. To address these needs for additional information about mutation analysis, we conducted an empirical study of the use of mutation analysis on two open source projects. Our results indicate that a focused effort on increasing mutation score leads to a corresponding increase in line and branch coverage to the point that line coverage, branch coverage and mutation score reach a maximum but leave some types of code structures uncovered. Mutation analysis guides the creation of additional “common programmer error” tests beyond those written to increase line and branch coverage. We also found that 74% of our chosen set of mutation operators is useful, on average, for producing new tests. The remaining 26% of mutation operators did not produce new test cases because their mutants were immediately detected by the initial test suite, indirectly detected by test suites we added to detect other mutants, or were not able to be detected by any test.

Social Propagation: Boosting Social Annotations for Web Mining

This paper is concerned with the problem of boosting social annotations using propagation, which is also called social propagation. In particular, we focus on propagating social annotations of web pages (e.g., annotations in Del.icio.us). Social annotations are novel resources and valuable in many web applications, including web search and browsing. Although they are developing fast, social annotations of web pages cover only a small proportion (<0.1%) of the World Wide Web. To alleviate the low coverage of annotations, a general propagation model based on Random Surfer is proposed. Specifically, four steps are included, namely basic propagation, multiple-annotation propagation, multiple-link-type propagation, and constraint-guided propagation. The model is evaluated on a dataset of 40,422 web pages randomly sampled from 100 most popular English sites and ten famous academic sites. Each page’s annotations are obtained by querying the history interface of Del.icio.us. Experimental results show that the proposed model is very effective in increasing the coverage of annotations while still preserving novel properties of social annotations. Applications of propagated annotations on web search and classification further verify the effectiveness of the model.     

Using formal methods to increase confidence in a home network system implementation: a case study

A home network system consists of multiple networked appliances, intended to provide more convenient and comfortable living for home users. Before being deployed, one has to guarantee the correctness, the safety, and the security of the system. Here, we present the approach chosen to validate the Java implementation of a home network system. We rely on the Java Modelling Language to formally specify and validate an abstraction of the system. This is a substantially revised version of our paper that appeared in the proceedings of the Workshop On Leveraging Applications of Formal Methods, Verification and Validation (ISoLA), Poitiers-Futuroscope, France, December 2007.     

Characterization of AlPO systems,precursors of the novel basic catalyst family AlPON

XPS and DRIFTS (diffuse reflectance infrared spectroscopy) spectra of AlPO systems, formally AlPO₄-Al₂O₃, obtained by the sol-gel method have been studied in order to understand their geometric and electronic structure. Both DRIFTS and XPS demonstrate that the acidbase character of these samples depends on a structural modification. For low phosphorus content an amorphous spinel-like solid is proposed. This geometric arrangement alters the electronic density of oxide ions and phosphorus cations and hence their Lewis acid-base properties with respect to the amorphous solid having aluminium and phosphorus only in tetrahedral arrangement.     

The Golden Touch by Light: A Finely Engineered Luminogen Empowering High Photoactivatable and Photodynamic Efficiency for Cancer Phototheranostics

Photoactivatable agent is a powerful tool in biomedicine studies due to high-precision spatiotemporal control of light. However, those previously reported agents generally suffer from short wavelength, fluorescence self-quenching effect, and the lack of photosensitizing property, which severely restrict their practical applications. To address these issues, molecular engineering of 1,4-dihydropyridine derivatives is conducted to obtain an optimized agent, namely TPA-DHPy-Py, which exhibits low oxidation potential, high photoactivation efficiency, and excellent type I/II combined photodynamic activity. Concurrently, its photoactivated counterpart is featured by aggregation-induced near-infrared emission and remarkable reactive oxygen species (ROS) production efficiency. Upon photoactivation, TPA-DHPy-Py is capable of precisely identifying cancer cells from co-culturing cancer cells and normal cells without the assistance of any extra targeting units, and in situ monitoring lipid droplets and endoplasmic reticulum alteration under ROS stress, as well as achieving fluorescent visualization of tumor in vivo with supremely high imaging contrast. Furthermore, the unprecedented performance on photodynamic cancer therapy is demonstrated by the significant inhibition of tumor growth. Therefore, the photoactivatable TPA-DHPy-Py with dual-organelle-targeted and excellent photodynamic activity associated with self-monitoring ability is highly promising for cancer theranostics in clinical trials.     

A NIR-II AIEgen-Based Supramolecular Nanodot for Peroxynitrite-Potentiated Mild-Temperature Photothermal Therapy of Hepatocellular Carcinoma

Compared to conventional photothermal therapy (PTT) which requires hyperthermia higher than 50 °C, mild-temperature PTT is a more promising antitumor strategy with much lower phototoxicity to neighboring normal tissues. However, the therapeutic efficacy of mild-temperature PTT is always restricted by the thermoresistance of cancer cells. To address this issue, a supramolecular drug nanocarrier is fabricated to co-deliver nitric oxide (NO) and photothermal agent DCTBT with NIR-II aggregation-induced emission (AIE) characteristic for mild-temperature PTT. NO can be effectively released from the nanocarriers in intracellular reductive environment and DCTBT is capable of simultaneously producing reactive oxygen species (ROS) and hyperthermia upon 808 nm laser irradiation. The generated ROS can further react with NO to produce peroxynitrite (ONOOˉ) bearing strong oxidization and nitration capability. ONOOˉ can inhibit the expression of heat shock proteins (HSP) to reduce the thermoresistance of cancer cells, which is necessary to achieve excellent therapeutic efficacy of DCTBT-based PTT at mild temperature (<50 °C). The antitumor performance of ONOOˉ-potentiated mild-temperature PTT is validated on subcutaneous and orthotopic hepatocellular carcinoma (HCC) models. This research puts forward an innovative strategy to overcome thermoresistance for mild-temperature PTT, which provides new inspirations to explore ONOOˉ-sensitized tumor therapy strategies.     

Investigations on mode-division multiplexed free-space optical transmission for inter-satellite communication link

Wireless Networks - Inter-satellite data transmission links are very crucial for providing global inter-connectivity. We report designing and investigations on high date rate inter-satellite...     

Evolutionary state-based novel multi-objective periodic bacterial foraging optimization algorithm for data clustering

Clustering divides objects into groups based on similarity. However, traditional clustering approaches are plagued by their difficulty in dealing with data with complex structure and high dimensionality, as well as their inability in solving multi-objective data clustering problems. To address these issues, an evolutionary state-based novel multi-objective periodic bacterial foraging optimization algorithm (ES-NMPBFO) is proposed in this article. The algorithm is designed to alleviate the high-computing complexity of the standard bacterial foraging optimization (BFO) algorithm by introducing periodic BFO. Moreover, two learning strategies, global best individual (gbest) and personal historical best individual (pbest), are used in the chemotaxis operation to enhance the convergence speed and guide the bacteria to the optimum position. Two elimination-dispersal operations are also proposed to prevent falling into local optima and improve the diversity of solutions. The proposed algorithm is compared with five other algorithms on six validity indexes in two data clustering cases comprising nine general benchmark datasets and four credit risk assessment datasets. The experimental results suggest that the proposed algorithm significantly outperforms the competing approaches. To further examine the effectiveness of the proposed strategies, two variants of ES-NMPBFO were designed, and all three forms of ES-NMPBFO were tested. The experimental results show that all of the proposed strategies are conducive to the improvement of solution quality, diversity and convergence.     

Optimal imperfect preventive maintenance policy for equipment leased during successive periods

In this paper, we consider randomly failing equipment leased several times during their life cycle with a given warranty period. A mathematical model is developed to determine the optimal efficiency levels of preventive maintenance (PM) to be performed on the equipment between successive lease periods, maximising the expected total profit of the lessor over the equipment life cycle. The model considers the expected leasing revenue as well as the equipment acquisition cost and the average PM and repair costs. PM actions allow reducing the age of the equipment to a certain extent with a corresponding cost depending on the PM level adopted. The efficiency of the PM is determinant of the expected revenue during the next lease period. Given a set of K possible PM levels and the number of lease periods n over the equipment life cycle, K^{n?1 PM} strategies are possible. A genetic algorithm is proposed in order to obtain nearly optimal policies in situations where the number of possibilities K^n?1 is very high. Obtained numerical results are discussed. Small- and big-size instances of the problem are considered in the case of a service company in the oil and gas industry specialised in leasing specific equipment such as separators, to oil companies for production activities with a limited duration of several months like well testing or short production tests.