User feedback on software usage is utilised by developers to improve their software. Software product forums are platforms rich in software-related user feedback, such as forum threads containing bug reports or requests for new features. However, previous studies have mainly focused on analysing user feedback from software product forums as individual sentences, which can lead to missing insights and a lack of understanding of the overall context of forum posts. To fill this gap in research, this work examines user feedback found in software product forum posts to investigate the differences between content classifications found in forum sentences and posts. We manually evaluated software product forum posts collected from two open-sourced software product forums and discovered five new types of user feedback that can only be identified when examining user feedback in the form of forum posts. Additionally, we examined the association between sentence classifications found within software product forums. Our results indicate that contextual information complimenting product improvement insights can be found in software product forums, with a confidence of 0.75 and 0.69 for the association between apparent bug and application usage sentences. This information can be used to reduce manual efforts required to chase up missing contextual information when attempting to understand or fix software issues. We also provide insights into the progression of posts in software product forums at the thread-level, and our progression flowchart can be used to summarise the sequence of events in software product forum threads. Our findings reveal the importance of looking at user feedback within software product forums in the format of forum posts to identify new insights on user feedback for software improvements. Editor’s note: Open Science material was validated by the Journal of Systems and Software Open Science Board.

User feedback is an important resource in modern software development, often containing requirements that help address user concerns and desires for a software product. The feedback in online channels is a recent focus for software engineering researchers, with multiple studies proposing automatic analysis tools. In this work, we investigate the product forums of two large open source software projects. Through a quantitative analysis, we show that forum feedback is often manually linked to related issue tracker entries and product documentation. By linking feedback to their existing documentation, development teams enhance their understanding of known issues, and direct their users to known solutions. We discuss how the links between forum, issue tracker, and product documentation form a requirements ecosystem that has not been identified in the previous literature. We apply state-of-the-art deep-learning to automatically match forum posts with related issue tracker entries. Our approach identifies requirement matches with a mean average precision of 58.9% and hit ratio of 82.2%. Additionally, we apply deep-learning using an innovative clustering technique, achieving promising performance when matching forum posts to related product documentation. We discuss the possible applications of these automated techniques to support the flow of requirements between forum, issue tracker, and product documentation.

Feedback from software users is vital for engineering better software requirements. One tool for extracting requirements from online user feedback is clustering, where the most mentioned topics are found by grouping similar feedback together. For these topics to be understood, clusters have been summarized in previous work using characterizing phrases or sentences. This work evaluates which method of characterization (unigrams, bigrams, trigrams, or sentences) is most effective for understanding the semantic meaning of a whole cluster using feedback from multiple feedback sources. We evaluate multiple characterization methods to determine the ability of each method to create distinct, descriptive characterizations. We further evaluate the amount of requirements relevant characterizations created by each characterization method. We find that unigrams, bigrams, trigrams, and full sentences all perform similarly in distinguishing clusters from each other. However, we find that fewer and more expressive characterizations, such as full sentences, contain more requirements relevant information from a feedback cluster compared to more numerous but less expressive unigrams, meaning a sentence will better summarize the important requirement relevant information from a cluster. Our findings inform the future development of user feedback clustering tools, with different cluster characterization methods being quantitatively measured for the first time.

Recent advancements in performance, logic density, and power consumption of Field-Programmable Gate Arrays (FPGAs) have made them attractive for their widespread adoption into automotive, aircraft, space, military, and other safety-critical applications, in both embedded systems and cloud computing platforms. Every year, though, it becomes harder and harder to benefit from such advances in technology scaling due to smaller voltage margins, more aggressive clocking schemes, and greater device variability. FPGAs are often expected to last years or even decades in a variety of different environments before replacement. In some applications, they can be susceptible to soft and transient errors due to Single Event Upsets (SEUs), environment, and aging related effects. In this paper, we propose a simplified modular arithmetic technique based upon the concept of the digital root (DR) to monitor soft and transient errors, with low area overhead and high rates of detectability. The technique can be easily implemented at the register-transfer level (RTL) with no need to modify the underlying hardware of the FPGA. In one experiment, after dropping the supply voltage well below recommended design margins, we show in situ measurements on the instantaneous error rate in an Intel Arria 10 GX FPGA, which can be leveraged to optimize the power-performance trade-off of already deployed designs. We demonstrate this tradeoff, using an inherently error tolerant low-density parity-check (LDPC) decoder block, by either increasing the system clock beyond its synthesized target to achieve a 50% improvement in throughput, or by lowering the FPGA's supply voltage below synthesized design margins for a 65% reduction in power.

This paper presents the design of a fully self-timed 8-bit 80-Ms/s single-core SAR ADC with interleaved comparators and a high order compensated opamp-less bandgap reference. A 3/2 interleaving algorithm was designed for better SFDR performance, where two of the three comparators are orderly chosen for interleaving in each conversion while offset calibration is applied to the idle comparator. Asynchronized SAR logic with a DAC settling timer is designed for fully self-timing of the ADC. The ADC was designed with a PVT stabilized on-chip reference source which promises a stable reference for the ADC at extreme temperature environments such as aerospace exploration or quantum computing. Technique for compensating the temperature coefficient (TC) of a bandgap reference (BGR) using temperature characteristics of transistor's current gain β is proposed. Measured results show 42.8dB SNDR, 56.8 dB SFDR and -53.3dBc THD for the proposed SAR ADC, drawing 1.07mW from a 1.1v supply. Measured average TC of the HBT proposed BGR is 23ppm/°C and 39 ppm/°C over the commercial (0~70°C) and space (-260~125°C) temperature ranges, respectively. The BGR reaches PSRR of -50dB at 1MHz, and -38dB at 1GHz. The entire chip was implemented on 0.13um 8HP SiGe process with an active area of 0.2952 mm2.

The modern software landscape is highly competitive. Software companies need to quickly fix reported bugs and release requested new features, or they risk negative reviews and reduced market share. The amount of online user feedback prevents manual analysis. Past research has investigated automated requirement mining techniques on online platforms like App Stores and Twitter, but online product forums have not been studied. In this paper, we show that online product forums are a rich source of user feedback that may be used to elicit product requirements. The information contained in forum questions is different from what has been described in the related work on App Stores or Twitter. Users often provide detailed context to specific problems they encounter with a software product and other users respond with workarounds or to confirm the problem. Through the analysis of two large forums, we identify 18 different types of information (classifications) contained in forums that can be relevant to maintenance and evolution tasks. We show that a state-of-the-art App Store tool is unable to accurately classify forum data, which may be due to the differences in content. Thus, specific techniques are likely needed to mine requirements from product forums. In an exploratory study, we developed classifiers with forum specific features. Promising results are achieved for all classifiers with f-measure scores ranging from 70.3% to 89.8%.