Software architectural changes involve more than one module or component and are complex to analyze compared to local code changes. Development teams aiming to review architectural aspects (design) of a change commit consider many essential scenarios such as access rules and restrictions on usage of program entities across modules. Moreover, design review is essential when proper architectural formulations are paramount for developing and deploying a system. Untangling architectural changes, recovering semantic design, and producing design notes are the crucial tasks of the design review process. To support these tasks, we construct a lightweight tool [4] that can detect and decompose semantic slices of a commit containing architectural instances. A semantic slice consists of a description of relational information of involved modules, their classes, methods and connected modules in a change instance, which is easy to understand to a reviewer. We extract various directory and naming structures (DANS) properties from the source code for developing our tool. Utilizing the DANS properties, our tool first detects architectural change instances based on our defined metric and then decomposes the slices (based on string processing). Our preliminary investigation with ten open-source projects (developed in Java and Kotlin) reveals that the DANS properties produce highly reliable precision and recall (93-100%) for detecting and generating architectural slices. Our proposed tool will serve as the preliminary approach for the semantic design recovery and design summary generation for the project releases.

Causes of software architectural change are classified as perfective, preventive, corrective, and adaptive.Change classification is used to promote common approaches for addressing similar changes, produce appropriate design documentation for a release, construct a developer's profile, form a balanced team, support code review, etc.However, automated architectural change classification techniques are in their infancy, perhaps due to the lack of a benchmark dataset and the need for extensive human involvement.To address these shortcomings, we present a benchmark dataset and a text classifier for determining the architectural change rationale from commit descriptions.First, we explored source code properties for change classification independent of project activity descriptions and found poor outcomes.Next, through extensive analysis, we identified the challenges of classifying architectural change from text and proposed a new classifier that uses concept tokens derived from the concept analysis of change samples.We also studied the sensitivity of change classification of various types of tokens present in commit messages.The experimental outcomes employing 10fold and cross-project validation techniques with five popular open-source systems show that the F1 score of our proposed classifier is around 70%.The precision and recall are mostly consistent among all categories of change and more promising than competing methods for text classification.

Continuous architecture is vital for developing large, complex software systems and supporting continuous delivery, integration, and testing practices. Researchers and practitioners investigate models and rules for managing change to support architecture continuity. They employ manual techniques to analyze software change, categorizing the changes as perfective, corrective, adaptive, and preventive. However, a manual approach is impractical for analyzing systems involving thousands of artefacts as it is time-consuming, labor-intensive, and error-prone. In this paper, we investigate whether an automatic technique incorporating free-form natural language text (e.g., developers' communication and commit messages) is an effective solution for architectural change analysis. Our experiments with multiple projects showed encouraging results for detecting architectural messages using our proposed language model. Although architectural change categorization for the preventive class is moderate, the outcome for the random dataset is insignificant in general (around a 45% F1 score). We investigated the causes of the unpromising outcome. Overall, our study reveals that our automated architectural change analysis tool would be fruitful only if the developers provide considerable technical details in the commit messages or other text.

The domain of plant genotyping and phenotyping presents a number of challenges in the area of large data computation. Various tools and systems have been developed to automate the scientific workflows and support the computational needs of this domain. In this paper, we review a number of the popular systems (i.e., Galaxy, iPlant, GenAp and LemnaTec) in the domain of plant genotyping and phenotyping using the scenario-based architectural analysis method (SAAM). In particular, we focus on how different stakeholders are using these systems in a variety of scenarios and to what extent the systems support their needs. Our SAAM analysis shows that the existing systems have shortcomings. For example, they are limited in their support for high throughput processing of large amounts of heterogeneous types of data. Based on our findings we propose a reference architecture along with a preliminary evaluation in the subject domain. The reference architecture and its evaluation is aimed at helping developers/architects create suitable architectural designs and select appropriate technologies when developing plant phenotyping and genotyping systems.

Real-time outdoor navigation in highly dynamic environments is an crucial problem. The recent literature on real-time static SLAM don't scale up to dynamic outdoor environments. Most of these methods assume moving objects as outliers or discard the information provided by them. We propose an algorithm to jointly infer the camera trajectory and the moving object trajectory simultaneously. In this paper, we perform a sparse scene flow based motion segmentation using a stereo camera. The segmented objects motion models are used for accurate localization of the camera trajectory as well as the moving objects. We exploit the relationship between moving objects for improving the accuracy of the poses. We formulate the poses as a factor graph incorporating all the constraints. We achieve exact incremental solution by solving a full nonlinear optimization problem in real time. The evaluation is performed on the challenging KITTI dataset with multiple moving cars. Our method outperforms the previous baselines in outdoor navigation.

Software developers often ask questions in Stack Overflow Q & A site, and their posted questions sometimes do not meet the standard guidelines.As a consequence, some of the questions are edited by expert users, some of them are down-voted, or some are even deleted permanently.Besides, the users (i.e., developers) might not get the expected solutions for their problems.In this paper, we study up-voted and down-voted questions from Stack Overflow, and analyze the relationship of embedded emotions with question quality.We use Sentiment140 API for identifying embedded emotions in the question texts, and then apply Feed-Forward Multilayer Perceptron (MLP) and Support Vector Machine (SVM) on the emotion data for developing a quality prediction model.Experiments using 38,920 Stack Overflow questions suggest about 70% precision and about 74% recall for our model with 10-fold cross-validation, and these findings clearly reveal the impact of human emotions upon the quality of a question.