Annervaz K M, Somnath Basu Roy Chowdhury, Ambedkar Dukkipati. Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers). 2018.

Duplicate Bug Detection is the problem of identifying whether a newly reported bug is a duplicate of an existing bug in the system and retrieving the original or similar bugs from the past. This is required to avoid costly rediscovery and redundant work. In typical software projects, the number of duplicate bugs reported may run into the order of thousands, making it expensive in terms of cost and time for manual intervention. This makes the problem of duplicate or similar bug detection an important one in Software Engineering domain. However, an automated solution for the same is not quite accurate yet in practice, in spite of many reported approaches using various machine learning techniques. In this work, we propose a retrieval and classification model using Siamese Convolutional Neural Networks (CNN) and Long Short Term Memory (LSTM) for accurate detection and retrieval of duplicate and similar bugs. We report an accuracy close to 90% and recall rate close to 80%, which makes possible the practical use of such a system. We describe our model in detail along with related discussions from the Deep Learning domain. By presenting the detailed experimental results, we illustrate the effectiveness of the model in practical systems, including for repositories for which supervised training data is not available.

Ontology, the shared formal conceptualization of domain information, has been shown to have multiple applications in modeling, processing and understanding natural language text. In this work, we use distributed word vectors out of various recent language models from Deep Learning for semi-automated domain ontology creation for closed domains. We cover all major aspects of Domain Ontology Induction or Learning like concept identification, attribute identification, taxonomical and non-taxonomical relationship identification using the distributed word vectors. Preliminary results show that simple clustering based methods using distributed word vectors from these language models outperforms methods using models like LSI in ontology learning for closed domains.

We propose a novel cloud-based data storage solution framework named Data Vaporizer (DV). The proposed framework provides many unique features such as storing data over multiple clouds or storage zones, resistance against organized vendor attacks, maintaining data integrity and confidentiality through client-side processing, fault-tolerance against failure of one or more cloud storage locations and avoids vendor lock-in of data. Data Vaporizer is highly configurable to meet various client data encryption requirements, compliance to industry standards and fault tolerance constraints depending on the nature and sensitivity of the data. To enhance the level of security and reliability, especially to protect data against malicious attacks and secure key management in cloud, DV uses advanced techniques of secret sharing of the keys. The architecture and optimality of data placement and efficient key management algorithm of DV ensure that the solution is highly scalable. The data foot print and subsequent cost incurred by our storage solution is minimal, considering the benefits provided. The initial response for the adoption of DV in actual client scenarios is promising.

Management of legal contracts in various business domains such as Real Estate are examples of typical business process outsourcing activity. One of such process is Lease Abstraction, where largely manual inspection and validation of large commercial lease documents made for real estate deals is done by offshore experts and relevant information from the documents is extracted into a structured form. This structured information is further used for aggregate analytics and decision making by large real estate firms. We propose a system based on machine learning techniques to semi automate this process, essentially leading to 50% human effort savings. Our approach weaves together state-of-the-art machine learning techniques like supervised classifier models, sequence modeling techniques and various semi-supervised approaches. We articulate the effectiveness of our solution using the results from the experiments. Our platform is being used in production environment by Accenture Operations and the initial results and user feedback are encouraging.

Knowledge Transition (KT) of legacy applications is a critical activity, often determining the quality of maintenance in the early stages of a maintenance life-cycle. We developed an integrated reverse engineering tool-suite that bootstraps the KT process by providing knowledge recipients insights to application structure, quality and functionality. The tool-suite is based on an in-depth study with KT practitioners and a comparative study of existing tools. We evaluated the benefits of the tool-suite during KT in real-life projects. In this talk, we report our learning from the study and evaluation phases.

Quality of requirements written in natural language has always been a critical concern in software engineering. Poorly written requirements lead to ambiguity and false interpretation in different phases of a software delivery project. Further, incomplete requirements lead to partial implementation of the desired system behavior. In this paper, we present a model for harvesting domain (functional or business) knowledge. Subsequently we present natural language processing and ontology based techniques for leveraging the model to analyze requirements quality and for requirements comprehension. The prototype also provides an advisory to business analysts so that the requirements can be aligned to the expected domain standard. The prototype developed is currently being used in practice, and the initial results are very encouraging.

Source code clustering is an important technique used in software development and maintenance to understand the modular structure of code. An array of algorithms are available for clustering like simulated annealing based search. Source code have different kinds of features such as structural or textual features. The collection of these different types of source code features and computation of relevant feature metrics is a difficult task. Further, the clustering algorithms can run on metrics based on different types of source code features or their combinations. This flexibility makes it non-trivial to test effectiveness of clustering algorithms on a source code. In this paper, we present a highly configurable clustering workbench that allows the user to collect the various source code features and then to select the code features used for clustering, the clustering algorithm and its various parameters. Clustering quality metrics are computed. They allow comparison of algorithm output based on different combinations of code-features and algorithms. We also present the specific contribution made in multi-dimensional feature analysis and clustering. The tool hides the algorithm complexity from the user, thus allowing complete focus on understanding the 'effect' of the configuration choices. We have also applied this tool in real-life maintenance projects, where the users found it useful to tweak the clustering techniques for the source-code peculiarities.

Maintaining the state of applications and user sessions is difficult in large scale web-based software systems. This problem is particularly accentuated in the context of Cloud computing as Cloud providers, especially Platform as a Service (PaaS) vendors, do not explicitly support state management infrastructure - such as clustering. In a PaaS environment, a user has little or no access and control over the server platform and session management layer. Additionally, the platform tiers are generally loosely coupled and service-oriented. These make traditional session-state management techniques non-usable. In this work, we present ReLoC - a session-state management architecture for Cloud that uses loosely-coupled services and platform agnostic scalable messaging technology to propagate and save session states. Preliminary experiments show a very high level of tolerance to failures of the platform tiers without corresponding disruptions in user sessions. We argue that, in the context of PaaS Clouds, ReLoC architecture will be more scalable compared to traditional clustering environments.