Effective bug detection and localization using information retrieval

Software bugs pose a fundamental threat to the reliability of software systems, even in systems designed with the best software engineering (SE) teams using the best SE practices. Detecting bugs early and fixing them quickly are extremely important. However, they are very expensive and challenging, especially at-scale. While the sciences of bug detection (e.g., software testing) and localization via static and dynamic program analyses have been explored considerably, text-based Information Retrieval (IR) techniques for bug detection and localization are interesting and promising new approaches for these problems. One advantage of text-based approaches is that it can utilize a lot of (implicit) semantic information about a program’s functionality from the program text, which is almost impossible to extract using program analysis based techniques. This dissertation builds a deeper understanding of current bug triaging and fixing processes via mining software repositories, and introduces new techniques for effective bug detection and localization. The dissertation has three main parts. First, we perform a number of empirical studies to investigate the extent of and reasons for long lived bugs, their severities, and time spent in different phases of bug fixing process. We demonstrate that many bugs remain unfixed for inordinate period of time due to numerous reasons, including difficulties in detecting, localizing, and fixing them. Second, we demonstrate that developers use very similar program text in source code and their corresponding test cases, which could be utilized to implement powerful test prioritization techniques. We introduce a novel IR based regression test prioritization technique called REPiR that embodies our insight, and show that REPiR is more efficient than program analysis based or dynamic coverage based techniques. Third, we demonstrate that fine grained program text such as class names, method names, variable names, and comments carry different levels of information, and it can be utilized to improve IR based bug localization. We introduce a structured retrieval technique called BLUiR that embodies our insights and show that BLUiR outperforms the existing state-of-the-art IR-based bug localization approaches. Finally, we further improve BLUiR by natural language processing. We make four contributions in this dissertation. One, we provide empirical evidence that there are considerable numbers of non-trivial bugs in software projects that survive for a long time. We describe the reasons for delay in fixing, the nature of fixes, and overall fixing process of these long lived bugs in a great detail. Two, we introduce the notion of IR-based regression test prioritization based on program changes. Three, we introduce the notion of structured retrieval for bug localization. Four, we provide an in-depth analysis of the extent to which natural languages processing can play an important role in improving IR-based bug localization further. The central ideas are embodied in a suite of prototype tools. Rigorous empirical evaluation is performed to validate the efficacy of the proposed techniques using datasets containing a variety of real-world Java and C programs.Electrical and Computer Engineerin

Development of Material Requirements Planning (MRP) Software with C Language

Now a day2019;s a number of manufacturing firms in developing countries do not practice affordable, efficient and user friendly inventory management tools which has been identified as a major cause of high inventory cost for adequate planning. This study focuses on the development of Material Requirements Planning (MRP) software with programming language C that can be used by the local industries for inventory management in a job shop manufacturing environment. An algorithm has been developed to understand the MRP processing logic. A manual method of calculation to solve MRP problem has also been shown. Evaluation tests of the software were carried out using various products ranging from those with simple structure of single product to complex structure. The software was shown to be user friendly and allow for easy data input and output to be saved and retrieved for future planning. The input process of the software has been shown step by step. The output of the program shows the time-phased requirements for assemblies, parts and raw materials as well as the missing deliveries and time required to meet the missing deliveries

Electrocorticography based motor imagery movements classification using long short-term memory (LSTM) based on deep learning approach

Brain–computer interface (BCI) is an important alternative for disabled people that enables the innovative communication pathway among individual thoughts and different assistive appliances. In order to make an efficient BCI system, different physiological signals from the brain have been utilized for instances, steady-state visual evoked potential, motor imagery, P300, movement-related potential and error-related potential. Among these physiological signals, motor imagery is widely used in almost all BCI applications. In this paper, Electrocorticography (ECoG) based motor imagery signal has been classified using long short-term memory (LSTM). ECoG based motor imagery data has been taken from BCI competition III, dataset I. The proposed LSTM approach has achieved the classification accuracy of 99.64%, which is the utmost accuracy in comparison with other state-of-art methods that have employed the same data set

Management and socio-economic conditions of fishermen of the Baluhar Baor, Jhenaidah, Bangladesh

This study was conducted on the management of the Baluhar Baor and fishermen’s socio-economic conditions of the baor in Jhenaidah district, Bangladesh. Data were collected by interviews, FGDs and CIs with key informants. This baor was managed under Oxbow Lake Project-1 of Department of Fisheries of Bangladesh government. Hypophthalmichthys molitrix, Labeo rohita, Catla catla, Cirrhina cirrhosus, Cyprinus carpio and Ctenopharyngodon idella were commonly stocked at the composition of 34%, 13%, 12%, 12%, 15% and 14%, respectively. Kochal, komor and chack fishing were used for harvesting and yearly production was 750 kg/ha. While studying the socio-economics, 58% fishermen were lived in joint families. 78% fishermen used kancha sanitary latrine which reflects their poor hygienic condition but they used tubewell for drinking water. 58% fishermen were with 0.041 hectare lands and 74% lived in kancha house. The annual income varied from BDT 15,000 to 60,000. Education level was found very low and only 18% completed their primary education. Majority fishermen (82%) visited village doctor for health services due to low income and lack of knowledge. All fishermen were fully dependent on baor fishery for their livelihood. It is possible to uplift their socio-economic by managing the baor with improved technology

Management and socio-economic conditions of fishermen of the Baluhar Baor, Jhenaidah, Bangladesh

This study was conducted on the management of the Baluhar Baor and fishermen’s socio-economic conditions of the baor in Jhenaidah district, Bangladesh. Data were collected by interviews, FGDs and CIs with key informants. This baor was managed under Oxbow Lake Project-1 of Department of Fisheries of Bangladesh government. Hypophthalmichthys molitrix, Labeo rohita, Catla catla, Cirrhina cirrhosus, Cyprinus carpio and Ctenopharyngodon idella were commonly stocked at the composition of 34%, 13%, 12%, 12%, 15% and 14%, respectively. Kochal, komor and chack fishing were used for harvesting and yearly production was 750 kg/ha. While studying the socio-economics, 58% fishermen were lived in joint families. 78% fishermen used kancha sanitary latrine which reflects their poor hygienic condition but they used tubewell for drinking water. 58% fishermen were with 0.041 hectare lands and 74% lived in kancha house. The annual income varied from BDT 15,000 to 60,000. Education level was found very low and only 18% completed their primary education. Majority fishermen (82%) visited village doctor for health services due to low income and lack of knowledge. All fishermen were fully dependent on baor fishery for their livelihood. It is possible to uplift their socio-economic by managing the baor with improved technology

AI-based automated Meibomian gland segmentation, classification and reflection correction in infrared Meibography

Purpose: Develop a deep learning-based automated method to segment meibomian glands (MG) and eyelids, quantitatively analyze the MG area and MG ratio, estimate the meiboscore, and remove specular reflections from infrared images. Methods: A total of 1600 meibography images were captured in a clinical setting. 1000 images were precisely annotated with multiple revisions by investigators and graded 6 times by meibomian gland dysfunction (MGD) experts. Two deep learning (DL) models were trained separately to segment areas of the MG and eyelid. Those segmentation were used to estimate MG ratio and meiboscores using a classification-based DL model. A generative adversarial network was implemented to remove specular reflections from original images. Results: The mean ratio of MG calculated by investigator annotation and DL segmentation was consistent 26.23% vs 25.12% in the upper eyelids and 32.34% vs. 32.29% in the lower eyelids, respectively. Our DL model achieved 73.01% accuracy for meiboscore classification on validation set and 59.17% accuracy when tested on images from independent center, compared to 53.44% validation accuracy by MGD experts. The DL-based approach successfully removes reflection from the original MG images without affecting meiboscore grading. Conclusions: DL with infrared meibography provides a fully automated, fast quantitative evaluation of MG morphology (MG Segmentation, MG area, MG ratio, and meiboscore) which are sufficiently accurate for diagnosing dry eye disease. Also, the DL removes specular reflection from images to be used by ophthalmologists for distraction-free assessment.Comment: 11 pages, 13 Figures, 5 Supplementary Figure