Substantial net improvements in programming quality and productivity have been obtained through the use of formal inspections of design ana of code. Improvements are made possible by a systematic and efficient design and code verification process, with well-defined roles for inspection participants. The manner in which inspection data is categorized and made suitable for process analysis is an important factor in attaining the improvements. It is shown that by using inspection results, a mechanism for initial error reduction followed by ever-improving error rates can be achieved.

Design and Code Inspections to Reduce Errors in Program Development (Reprint).

Crowdtesting has become an effective alternative to traditional testing, especially for mobile applications. However, crowdtesting is hard to manage in nature. Given the complexity of mobile applications and unpredictability of distributed crowdtesting processes, it is difficult to estimate (a) remaining number of bugs yet to be detected or (b) required cost to find those bugs. Experience-based decisions may result in ineffective crowdtesting processes, e.g., there is an average of 32% wasteful spending in current crowdtesting practices. This paper aims at exploring automated decision support to effectively manage crowdtesting processes. It proposes an approach named iSENSE which applies incremental sampling technique to process crowdtesting reports arriving in chronological order, organizes them into fixed-size groups as dynamic inputs, and predicts two test completion indicators in an incremental manner. The two indicators are: 1) total number of bugs predicted with Capture-ReCapture model, and 2) required test cost for achieving certain test objectives predicted with AutoRegressive Integrated Moving Average model. The evaluation of iSENSE is conducted on 46,434 reports of 218 crowdtesting tasks from one of the largest crowdtesting platforms in China. Its effectiveness is demonstrated through two application studies for automating crowdtesting management and semi-automation of task closing trade-off analysis. The results show that iSENSE can provide managers with greater awareness of testing progress to achieve cost-effectiveness gains of crowdtesting. Specifically, a median of 100% bugs can be detected with 30% saved cost based on the automated close prediction.

iSENSE: completion-aware crowdtesting management

Inspections are a proven approach for improving software requirements quality. Owing to the fact that inspectors report both faults and non-faults (i.e., false-positives) in their inspection reports, a major chunk of work falls on the person who is responsible for consolidating the reports received from multiple inspectors. We aim at automation of fault-consolidation step by using supervised machine learning algorithms that can effectively isolate faults from non-faults. Three different inspection studies were conducted in controlled environments to obtain real inspection data from inspectors belonging to both industry and from academic backgrounds. Next, we devised a methodology to separate faults from non-faults by first using ten individual classifiers from five different classification families to categorize different fault-types (e.g., omission, incorrectness, and inconsistencies). Based on the individual performance of classifiers for each fault-type, we created targeted ensembles that are suitable for identification of each fault-type. Our analysis showed that our selected ensemble classifiers were able to separate faults from non-faults with very high accuracy (as high as 85-89% for some fault-types), with a notable result being that in some cases, individual classifiers performed better than ensembles. In general, our approach can significantly reduce effort required to isolate faults from false-positives during the fault consolidation step of requirements inspections. Our approach also discusses the percentage possibility of correctly classifying each fault-type.

Validating Requirements Reviews by Introducing Fault-Type Level Granularity: A Machine Learning Approach

Background -- Inspecting requirements and design artifacts to find faults saves rework effort significantly. While inspections are effective, their overall team performance rely on inspectors' ability to detect and report faults. Our previous research showed that individual inspectors have varying LSs (i.e., they vary in their ability to process information recorded in requirements document). To extend the results of our previous LS research, this paper utilizes the concept of eye tracking (to record eye movements of inspectors) along with their LSs to detect reading patterns of inspectors during requirements inspections. Aim -- The objective of this research is to analyze the reading trends of effective and efficient inspectors using eye movement and LS data of individual inspectors and virtual inspection teams. Method -- The current research uses data (LS, eye tracking, and inspection) from thirteen inspectors to find its impact on inspection effectiveness and efficiency. Results -- Results from this study show that, inspectors who detect more faults during inspection, focus significantly more at the fault region to find and report faults as opposed to comprehending requirements information. Results also showed Inspection teams with diverse inspectors outperform similar teams and spend more time in comprehending information at the fault region. Additionally, results showed that inspectors with SEQ LS significantly tends to focus more at fault locations and are preferred for inspection. Conclusion -- These results can aid the selection of inspectors during the inspection process thus improving software quality

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Using Eye Tracking to Investigate Reading Patterns and Learning Styles of Software Requirement Inspectors to Enhance Inspection Team Outcome

Software inspections are most widely used technique by industrial practitioners for improving software quality. While inspections are an effective verification technique, evidence suggests that overall performance of an inspection process is highly dependent on an individual inspectors' ability to detect faults. Our work leverages cognitive science research to help improve the inspection team performance. This paper presents the results from two industrial studies that evaluate the effect of cognitive learning styles (LSs) of individual inspectors on their inspection team performance. The results showed that the inspection teams formed with inspectors of diverse LSs outperformed teams with similar LSs of inspectors. These results can help software managers better staff inspectors, enabling cost savings, and improving quality.

Using Learning Styles to Staff and Improve Software Inspection Team Performance

Inspections of software artifacts during early software development aids managers to detect early faults that may be hard to find and fix later. Results showed inspection ability does not depend on educational background and technical knowledge. This paper presents the results from an industrial empirical study, wherein the Learning Styles (i.e. ability to perceive and process information) of individual inspectors were manipulated to measure its impact on the fault detection effectiveness of inspection teams. Using inspection data from professional developers, we developed virtual teams with varying LS’s of individual inspectors and analyzed the team performance. The results from the current study show that teams of inspectors with diverse LS’s are significantly more effective at detecting faults as compared to teams of inspectors with similar LS’s. Therefore, LS’s can aid software managers to create high performance inspection team(s) and manage software quality.

Using Learning Styles of Software Professionals to Improve Their Inspection Team Performance

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Abhinav Singh

Papers

Using Learning Styles of Software Professionals to Improve Their Inspection Team Performance

Using Learning Styles of Software Professionals to Improve their Inspection Team Performance.