Let $p$ be an odd prime. The factorization of the polynomial $x^{p+1}-1$ over the integer residue ring $\mathbb{Z}_{p^e}$ is pivotal for constructing cyclic codes with Hermitian symmetry, a critical resource for Linear Complementary Dual (LCD) codes and Entanglement-Assisted Quantum Error-Correcting Codes (EAQECC). Traditionally, lifting factorizations relies on the generic Hensel's Lemma, masking the underlying algebraic structure. In this paper, we establish a structural isomorphism between the lifting process and the roots of a special auxiliary polynomial $V(x)$, unveiling a deterministic link to Dickson polynomials. Based on this theory, we develop \texttt{Dickson-Engine}, a linear-time algorithm ($O(ep)$) that outperforms standard libraries by orders of magnitude. Applying this engine to $\mathbb{Z}_{169}$, we explicitly construct a family of classical LCD codes of length $n=182$ via the isometric Gray map. Our search reveals codes with parameters (e.g., $[182, 1, 168]_{13}$ and $[182, 2, 144]_{13}$) that are \textbf{near-optimal} with respect to the theoretical Griesmer Bound. Notably, we discover a ``robustness plateau'' starting from non-trivial dimensions ($k=4$), where the minimum distance remains stable ($d=120$) even as the dimension triples ($k=4 \rightarrow 12$). These codes provide exceptional resources for post-quantum cryptography and quantum error correction without entanglement consumption ($c=0$).

API recommendation is crucial to improve programmers’ productivity. A lot of work has been proposed to improve the accuracy of API recommendations. In the existing work, many metrics, such as Precision, Recall, and MAP are used to evaluate the accuracy of the recommendation. These metrics can well reflect the ability to distinguish useful APIs from the candidate set, but they cannot evaluate the ability to determine the priority of useful APIs with each other. The priority between related APIs directly determines whether the recommended results are practical for developers. From this perspective, inspired by the sequence-aware recommendation, this paper constructs an API recommendation method with sequence awareness and designs new metrics to evaluate the method’s ability to determine the priority of useful APIs. The experimental results show that, compared with the baseline, the proposed method not only achieves better results on the common widely-used metrics but also outperforms the baseline method concerning the newly proposed sequence metrics.

Code summarization aims to automatically generate functionality descriptions of code snippets. Faithful metrics are needed to measure to which degree the machine generated summaries capture the semantics of the code snippets. Most commonly used metrics in code summarization, such as BLEU -4, METEOR, and ROUGE-L, originate from machine translation and text summarization, and have constantly been found to be inconsistent with human assessment. In this paper, we propose a novel evaluation metric, Consensus-based Code Summarization Evaluation (CCSE), which assigns different semantic weights to the n-grams of the summary. We also provide an algorithm to match the n-gram pairs from the reference and candidate based on the similarities. To validate the effectiveness of our proposed metric, we collect summary pairs from two public Java datasets and calculate the correlation coefficients between CCSE and the human evaluations. The experiment results show that, compared with BLEU-4, METEOR, and ROUGE-L, CCSE is more consistent with the scores assessed by human developers.

Automatic API recommendations can liberate software developers from labor-intensive programming tasks. Collaborative filtering (CF) techniques, which have been proved to be superior to other classic techniques, are widely used in recommendation tasks such as music, book, and goods recommendations, but are rarely used in the recommendation of APIs. In this paper, we employ the hybrid of CF techniques to build an API recommendation system. More precisely, We treat the API recommendation task as an item recommendation problem, where method declarations are regarded as users, API calls are regarded as items. First, we use the memory-based CF technique to find the most similar projects, collect the most similar declarations, and take API calls used by the considered declarations together to generate a rating matrix. Next, we use the model-based CF technique to complete the missing values in the rating matrix, then a ranked list of APIs is generated based on the completed rating matrix and sent to the developers as a recommendation result. Experimental results show that compared with the state-of-the-art work, the proposed approach can achieve better performance in terms of a comprehensive set of metrics, such as Success Rate, Precision, Recall, MRR, and NDCG for the top-1, top-3 and top-5 recommended APIs.

With the rapid development of the market for mobile apps, there are a number of apps with similar functions. To gain an advantage in such a competitive environment, developers need to understand not only the strengths and weaknesses of their app but also competitive apps. User reviews contain valuable information for comparing similar apps from user preference. In this paper, we propose UISAT (User-review mining via topic Identification, Sentiment Analysis and Topic matching across apps), an automated approach to compare user reviews from similar apps with the goal of mining user feedback from competitive apps by (i) extracting the hidden topics from large volumes of user reviews using topic modeling, (ii) combining a rule-based model, user rating and user-helpful for sentiment analysis of topics and (iii) matching relevant topics across apps. Empirical studies demonstrate that UISAT is effective and promising for developers to build and maintain a more competitive app.